DingoBorat
Slim
You trying to upset people Pom?
BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
Frangipani
Top 20
Less than two months ago, our then Regional Sales Manager in Taiwan, Edward Lien (who just announced that he had left our company) posted on LinkedIn that he were looking forward to attending 2025 Andes RISC-V CON Beijing:
View attachment 89691
I assume he was originally also scheduled to be the speaker representing BrainChip at that conference, which will take place on 27 August:
ANDES RISC-V CON Beijing | EventX
AI 与车用科技快速演进，推动业界对高效运算与资安技术的高度需求。 RISC-V 凭借其开放、弹性与可扩充架构，正成为高性能运算领域的关键解决方案。spot.eventx.io
View attachment 89692
If you had checked out the conference website over the past few days, you would have been greeted with this (screenshot taken on 6 August):
View attachment 89693
❤️🔥 Join us at Andes RISC-V CON Beijing on 8/27 for: | Andes Technology
❤️🔥 Join us at Andes RISC-V CON Beijing on 8/27 for: 👉 "RISC-V Ecosystem Panel Discussion: Accelerating AI Computing — Strengths and Challenges of RISC-V"! 🎯Moderated by Wei Wu, the Director of PLCT Lab, this panel brings together top minds from across the RISC-V and AI ecosystem: 🎯Simon...
CHIPS
Regular
Pom down under
Top 20
Fullmoonfever
Top 20
Maybe we need to be handing out pens with the potential contracts 
Couple excepts from a former researcher at Fraunhofer. Rest of article in the link.
www.linkedin.com
Nikhil Raj
+ Follow
Companies like BrainChip, GrAI Matter Labs, and Rain Neuromorphics are developing neuromorphic accelerators specifically designed for edge deployment, with power envelopes measured in milliwatts rather than watts. The barrier to entry is dropping fast.
For anyone building autonomous systems, robotics, or embedded AI: neuromorphic computing isn't a future consideration, it's a current competitive advantage. We're finally building machines that perceive like living systems: efficiently, reactively, and in real time.
Couple excepts from a former researcher at Fraunhofer. Rest of article in the link.
Why Neuromorphic Sensor Fusion is About to Change Everything
The Power Wall That's Reshaping Perception In the basement, a robotic arm equipped with event cameras, LiDAR sensors, and neuromorphic processors operates in near-complete darkness, tracking and manipulating objects with millisecond precision while consuming less power than a smartphone's display. T
Why Neuromorphic Sensor Fusion is About to Change Everything
Nikhil Raj
Nikhil Raj
Former Research Assistant at Fraunhofer IPM |…
Published Jul 31, 2025+ Follow
Companies like BrainChip, GrAI Matter Labs, and Rain Neuromorphics are developing neuromorphic accelerators specifically designed for edge deployment, with power envelopes measured in milliwatts rather than watts. The barrier to entry is dropping fast.
Bottom Line
Working with automotive and research sensor fusion systems, I've watched theoretical advantages become deployment realities. The hardware exists, algorithms work, and applications demand it.For anyone building autonomous systems, robotics, or embedded AI: neuromorphic computing isn't a future consideration, it's a current competitive advantage. We're finally building machines that perceive like living systems: efficiently, reactively, and in real time.
DingoBorat
Slim
It's probably already been mentioned, but c'mon let's do better!
❤️🔥 Join us at Andes RISC-V CON Beijing on 8/27 for: | Andes Technology
❤️🔥 Join us at Andes RISC-V CON Beijing on 8/27 for: 👉 "RISC-V Ecosystem Panel Discussion: Accelerating AI Computing — Strengths and Challenges of RISC-V"! 🎯Moderated by Wei Wu, the Director of PLCT Lab, this panel brings together top minds from across the RISC-V and AI ecosystem: 🎯Simon...
View attachment 89872
It appears to be still there as "Akita"..
I'd say it was us that filled in his bio and I understand that it was probably "typed in" as Akida and auto-corrected to Akita...
But should have been picked up and changed, at least by now??..
Cybersecurity is big business - Palo Alto Networks
https://www.msn.com/en-au/money/tec...S&cvid=12dffb149f584d1aa12028511d5d5960&ei=16
Security or nothing
Customers spending $150M pa EACH!
Need to resolve an attack in 25 minutes.
https://www.paloaltonetworks.com/
PAN has $121B market cap*
Cybersecurity needs prevention before cure - real time attack detection - who does that?
* Remember, cybersecurity is only a minor example of Akida's tricks.
https://www.msn.com/en-au/money/tec...S&cvid=12dffb149f584d1aa12028511d5d5960&ei=16
Security or nothing
Customers spending $150M pa EACH!
Need to resolve an attack in 25 minutes.
https://www.paloaltonetworks.com/
PAN has $121B market cap*
Cybersecurity needs prevention before cure - real time attack detection - who does that?
* Remember, cybersecurity is only a minor example of Akida's tricks.
Last edited:
supersonic001
Regular
Meet the Renesas RA8P1 MCU, our latest breakthrough in Edge AI and IoT. | Renesas Electronics
Meet the Renesas RA8P1 MCU, our latest breakthrough in Edge AI and IoT. Establishing a new performance level for MCUs, the RA8P1 combines a 1 GHz Arm® Cortex® M85 and 250 MHz Cortex M33 with the Arm Ethos U55 Neural Processing Unit (NPU)—delivering over 7300 CoreMarks and 256 GOPS at 500 MHz...
Or sure if posted, but are we there yet……. M85 ?
Meet the Renesas RA8P1 MCU, our latest breakthrough in Edge AI and IoT. | Renesas Electronics
Meet the Renesas RA8P1 MCU, our latest breakthrough in Edge AI and IoT. Establishing a new performance level for MCUs, the RA8P1 combines a 1 GHz Arm® Cortex® M85 and 250 MHz Cortex M33 with the Arm Ethos U55 Neural Processing Unit (NPU)—delivering over 7300 CoreMarks and 256 GOPS at 500 MHz...
Or sure if posted, but are we there yet……. M85 ?
View attachment 89878
Hi SS,
The Datasheet lists ARM ETHOS as the AI:
https://www.renesas.com/en/document/dst/25574255?r=25574019
Arm® EthosTM-U55 NPU
● Number of 8x8 MACs: 256 units
● Network: 8-bit and 16-bit integer quantized Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN)
● Compression: 8-bits weights
● Maximum operating frequency: 500 MHz
SharesForBrekky
Regular
U.S. Government to Take a 10% Equity Stake in Intel
According to the latest report from Bloomberg, the U.S. government is currently discussing taking a 10% stake in Intel Corp. Late last week, we reported on Intel's CEO Lip-Bu Tan's visit to the White House to discuss the future of Intel as America's most important semiconductor manufacturer. And...
"What the U.S. now plans is a 10% stake, roughly worth $10.5 billion, which would practically mean the same thing, with voting power over company decisions as its largest stakeholder."
"It is likely that the U.S. government could use this structure to go after Intel Foundry alone, as the primary focus of the government lies in leading-edge manufacturing."
Good evening from Perth...
It appears our old mates at Accenture Global Solutions Ltd have had a new Patent published just 3 weeks ago, naming us alongside Intel, but this time finally stating a true fact, Intel's Loihi is the research chip while we are the actual commercial chip, nice, finally
Maybe Dio or the crew have already posted this newish patent, but here is the link anyway.... when the f... will a company sign an IP contract...moan,
moan
It appears our old mates at Accenture Global Solutions Ltd have had a new Patent published just 3 weeks ago, naming us alongside Intel, but this time finally stating a true fact, Intel's Loihi is the research chip while we are the actual commercial chip, nice, finally
Maybe Dio or the crew have already posted this newish patent, but here is the link anyway.... when the f... will a company sign an IP contract...moan,
moan
DingoBorat
Slim
I wonder how many here would be happy if Trump got the US government to buy a share of BrainChip?U.S. Government to Take a 10% Equity Stake in Intel
According to the latest report from Bloomberg, the U.S. government is currently discussing taking a 10% stake in Intel Corp. Late last week, we reported on Intel's CEO Lip-Bu Tan's visit to the White House to discuss the future of Intel as America's most important semiconductor manufacturer. And...www.techpowerup.com
"What the U.S. now plans is a 10% stake, roughly worth $10.5 billion, which would practically mean the same thing, with voting power over company decisions as its largest stakeholder."
"It is likely that the U.S. government could use this structure to go after Intel Foundry alone, as the primary focus of the government lies in leading-edge manufacturing."
...With Trump's focus on military and A.I. dominance and our varied top of the line use cases within the military, it's not an outlandish possibility, especially in view of the above, taking a stake in Intel...
There would be plenty of mixed feelings here I'm sure
Fullmoonfever
Top 20
Straight forward well explained outline of neuromorphic in autos incl Akida.
Worth a read through imo hence the post.
Really highlights the hoops to jump through and hurdles to get over to eventually have neuromorphic acceptance in the auto sphere.
There is plenty happening as well in this space but this shows how long the development & regulatory cycle is...though, it is getting much closer it seems.
www.linkedin.com
Elmehdi CHOKRI
+ Follow
Neuromorphic computing represents the next evolution in automotive edge processing, delivering brain-inspired computing architectures that fundamentally change how vehicles process sensor data and make real-time decisions. Unlike traditional processors that consume power continuously, neuromorphic chips only consume energy when actively processing information—similar to how the human brain operates. This technology promises to solve the automotive industry's most pressing computational challenges: real-time processing of massive sensor data streams, ultra-low power consumption critical for electric vehicles, and adaptive intelligence that learns and improves over time.
Power Wall: Current GPU-based systems consume 200-500 watts for autonomous driving computations, severely impacting electric vehicle range and requiring complex cooling systems.
Latency Bottleneck: Safety-critical decisions must occur within 10 milliseconds, but traditional architectures struggle with this constraint while processing multiple high-bandwidth sensor streams simultaneously.
Adaptability Gap: Conventional systems require complete retraining and redeployment to adapt to new scenarios, making them inflexible for the dynamic automotive environment.
Processing this data in real-time using traditional architectures requires massive computational resources and generates significant heat—both problematic in automotive applications.
Key Operational Principles:
Event-Driven Architecture: Neuromorphic processors respond only to changes in sensor data. When a camera detects motion or a LiDAR point moves, the system activates. Static scenes consume virtually no power.
Parallel Processing: Unlike traditional sequential processing, neuromorphic systems process thousands of data streams simultaneously, similar to how the brain processes multiple sensory inputs at once.
Adaptive Learning: These systems learn and adapt continuously without requiring complete system shutdowns for updates—critical for automotive applications where learning must happen during operation.
Real-Time Response: Event-driven processing eliminates computational delays inherent in clocked systems. Sensor changes trigger immediate responses without waiting for clock cycles.
Graceful Degradation: If part of the neuromorphic network fails, the system continues operating with reduced capability rather than complete failure—essential for automotive safety.
Technical Specifications:
Automotive Applications:
Key Features:
Deployment Examples:
Architecture Highlights:
LiDAR Point Cloud Processing: Traditional systems struggle with the sparse, three-dimensional nature of LiDAR data. Neuromorphic processors excel at:
Radar Signal Processing: Modern automotive radar generates complex multi-dimensional data that neuromorphic systems handle efficiently:
Temporal Integration: The system learns how different sensors provide information at different times and integrates this temporal knowledge for more accurate scene understanding.
Confidence Weighting: The network automatically adjusts trust in different sensors based on environmental conditions—trusting cameras less in fog while relying more heavily on radar and LiDAR.
Predictive Processing: The system predicts sensor readings based on vehicle dynamics and scene understanding, allowing detection of sensor failures or anomalies.
Adaptive Path Planning: The system continuously learns optimal driving patterns:
Vibration and Shock Resistance: Automotive environments subject electronics to continuous vibration and occasional high-g shock events. Neuromorphic systems address this through:
Electromagnetic Compatibility: Modern vehicles contain numerous electronic systems that must coexist without interference:
ASIL-D Requirements (highest automotive safety level):
Safety Architecture Design:
AUTOSAR Compatibility: Integration with automotive software architectures requires:
Hardware-in-the-Loop Testing: Neuromorphic systems integrate with existing HIL test environments:
Custom Algorithm Development: Engineers can develop specialized neuromorphic algorithms:
Battery Life Impact: For electric vehicles, neuromorphic processing extends range:
Throughput Advantages: Event-driven processing handles more sensor streams:
Fleet Learning: Multiple vehicles can share learned behaviors:
OEM Pilot Programs: Automotive manufacturers are evaluating neuromorphic technology:
2026-2028: Mainstream Integration
2029-2032: Widespread Deployment
Verification and Validation: Ensuring neuromorphic system reliability:
Industry Standardization: Neuromorphic technology requires standard development:
Hybrid Processing Systems: Future vehicles may combine multiple processing approaches:
Competitive Advantages: Early adopters may gain significant advantages:
Resources & further readings:
The Mobility Chronicles
Worth a read through imo hence the post.
Really highlights the hoops to jump through and hurdles to get over to eventually have neuromorphic acceptance in the auto sphere.
There is plenty happening as well in this space but this shows how long the development & regulatory cycle is...though, it is getting much closer it seems.
Simple study notes about Neuromorphic computing in vehicle (automotive engineering) context.
Neuromorphic computing represents the next evolution in automotive edge processing, delivering brain-inspired computing architectures that fundamentally change how vehicles process sensor data and make real-time decisions. Unlike traditional processors that consume power continuously, neuromorphic c
Simple study notes about Neuromorphic computing in vehicle (automotive engineering) context.
Elmehdi CHOKRI
Elmehdi CHOKRI
Mechatronics Engineering | Electrical Systems |…
Published Aug 16, 2025+ Follow
Neuromorphic computing represents the next evolution in automotive edge processing, delivering brain-inspired computing architectures that fundamentally change how vehicles process sensor data and make real-time decisions. Unlike traditional processors that consume power continuously, neuromorphic chips only consume energy when actively processing information—similar to how the human brain operates. This technology promises to solve the automotive industry's most pressing computational challenges: real-time processing of massive sensor data streams, ultra-low power consumption critical for electric vehicles, and adaptive intelligence that learns and improves over time.
The Automotive computing challenge:
Current system limitations:
Modern vehicles generate unprecedented amounts of data. A single autonomous vehicle produces over 4 terabytes of sensor data daily from cameras, LiDAR, radar, and other sensors. Traditional automotive computing faces three critical bottlenecks:Power Wall: Current GPU-based systems consume 200-500 watts for autonomous driving computations, severely impacting electric vehicle range and requiring complex cooling systems.
Latency Bottleneck: Safety-critical decisions must occur within 10 milliseconds, but traditional architectures struggle with this constraint while processing multiple high-bandwidth sensor streams simultaneously.
Adaptability Gap: Conventional systems require complete retraining and redeployment to adapt to new scenarios, making them inflexible for the dynamic automotive environment.
Data Processing Reality
Consider a typical autonomous vehicle's sensor suite:- 8 cameras generating 24GB/s of raw video data
- Multiple LiDAR sensors producing 1GB/s of point cloud data
- 12+ radar sensors providing continuous range and velocity measurements
- IMU and GPS systems delivering high-frequency positioning data
Processing this data in real-time using traditional architectures requires massive computational resources and generates significant heat—both problematic in automotive applications.
Neuromorphic Computing Fundamentals
Brain-Inspired Processing
Neuromorphic processors mimic the human brain's efficiency by using event-driven processing. Instead of processing data continuously like traditional computers, neuromorphic chips only activate when something changes in the sensor data—just like how your brain doesn't actively think about things that aren't changing.Key Operational Principles:
Event-Driven Architecture: Neuromorphic processors respond only to changes in sensor data. When a camera detects motion or a LiDAR point moves, the system activates. Static scenes consume virtually no power.
Parallel Processing: Unlike traditional sequential processing, neuromorphic systems process thousands of data streams simultaneously, similar to how the brain processes multiple sensory inputs at once.
Adaptive Learning: These systems learn and adapt continuously without requiring complete system shutdowns for updates—critical for automotive applications where learning must happen during operation.
Practical Advantages for Automotive
Ultra-Low Power Consumption: Neuromorphic processors consume 10-100 times less power than equivalent GPU systems. A neuromorphic processing unit handling full autonomous driving computations might consume 5-15 watts versus 200-500 watts for traditional systems.Real-Time Response: Event-driven processing eliminates computational delays inherent in clocked systems. Sensor changes trigger immediate responses without waiting for clock cycles.
Graceful Degradation: If part of the neuromorphic network fails, the system continues operating with reduced capability rather than complete failure—essential for automotive safety.
Current Neuromorphic Processors for Automotive
Intel Loihi Platform
Intel's Loihi processors are being evaluated by major automotive OEMs for advanced driver assistance systems (ADAS). The Loihi architecture provides:Technical Specifications:
- 128 neuromorphic cores with distributed processing
- 130,000 artificial neurons and 130 million synapses
- Power consumption scaling from 30mW to 1W based on activity
- On-chip learning without external training
Automotive Applications:
- Real-time object detection and tracking
- Sensor fusion for multiple camera and radar inputs
- Adaptive cruise control with learning-based prediction
- Pedestrian and cyclist behavior prediction
BrainChip Akida Processors
BrainChip has developed commercially available neuromorphic processors specifically targeting edge AI applications including automotive:Key Features:
- Fully digital neuromorphic implementation for automotive reliability
- Sub-1-watt power consumption for complex AI workloads
- Incremental learning capabilities for continuous improvement
- Direct integration with existing automotive sensor interfaces
Deployment Examples:
- Advanced driver monitoring systems
- Real-time road sign recognition and interpretation
- Dynamic route optimization based on traffic patterns
- Predictive maintenance through vibration and sound analysis
IBM TrueNorth Architecture
IBM's TrueNorth provides a research platform for understanding neuromorphic computing's potential in automotive applications:Architecture Highlights:
- 4,096 neurosynaptic cores in a distributed mesh
- 1 million neurons with 256 million programmable synapses
- 65mW maximum power consumption
- Real-time processing with sub-millisecond response times
Automotive-Specific Applications
Advanced Sensor Processing
Dynamic Vision Sensors (DVS): Neuromorphic processors naturally interface with event-based cameras that only capture pixel changes rather than full frames. This combination provides:- Instant motion detection without motion blur
- Ultra-low latency response to moving objects
- Minimal data bandwidth requirements
- Superior performance in challenging lighting conditions
LiDAR Point Cloud Processing: Traditional systems struggle with the sparse, three-dimensional nature of LiDAR data. Neuromorphic processors excel at:
- Real-time 3D object detection and classification
- Efficient processing of sparse point clouds
- Temporal tracking of moving objects across frames
- Integration with camera data for enhanced perception
Radar Signal Processing: Modern automotive radar generates complex multi-dimensional data that neuromorphic systems handle efficiently:
- Real-time Doppler processing for velocity detection
- Multi-target tracking in dense traffic scenarios
- Interference rejection and signal cleanup
- Weather and environmental adaptation
Intelligent Sensor Fusion
Neuromorphic processors enable sophisticated sensor fusion that goes beyond simple data combination:Temporal Integration: The system learns how different sensors provide information at different times and integrates this temporal knowledge for more accurate scene understanding.
Confidence Weighting: The network automatically adjusts trust in different sensors based on environmental conditions—trusting cameras less in fog while relying more heavily on radar and LiDAR.
Predictive Processing: The system predicts sensor readings based on vehicle dynamics and scene understanding, allowing detection of sensor failures or anomalies.
Real-Time Decision Making
Behavioral Prediction: Neuromorphic systems excel at learning and predicting the behavior of other road users:- Pedestrian crossing intention detection
- Vehicle lane-change prediction
- Cyclist behavior modeling
- Emergency vehicle response patterns
Adaptive Path Planning: The system continuously learns optimal driving patterns:
- Driver preference learning for comfort optimization
- Traffic pattern recognition for efficient routing
- Weather condition adaptation
- Road surface condition response
Implementation Considerations
Automotive-Grade Requirements
Temperature Resilience: Automotive neuromorphic processors must operate reliably from -40°C to +85°C (extended range to +125°C for engine bay applications). Custom automotive implementations include:- Temperature-compensated neuron models
- Thermally-aware processing distribution
- Redundant critical pathways
- Adaptive performance scaling
Vibration and Shock Resistance: Automotive environments subject electronics to continuous vibration and occasional high-g shock events. Neuromorphic systems address this through:
- Solid-state implementation without moving parts
- Robust packaging designed for automotive stress
- Error correction for memory elements
- Graceful degradation under mechanical stress
Electromagnetic Compatibility: Modern vehicles contain numerous electronic systems that must coexist without interference:
- Low-noise neuromorphic designs
- Shielded packaging for sensitive components
- Filtering for power supply and communication lines
- Compliance with automotive EMC standards
Functional Safety Integration
ISO 26262 Compliance: Automotive neuromorphic systems must meet functional safety standards:ASIL-D Requirements (highest automotive safety level):
- Redundant processing pathways for safety-critical functions
- Continuous self-monitoring and diagnostic capabilities
- Fail-safe modes for detected failures
- Quantified failure rates and safety metrics
Safety Architecture Design:
- Dual-channel processing with comparison
- Watchdog timers for response monitoring
- Safe state transitions during failures
- External safety monitoring systems
Integration with Existing Systems
CAN and Automotive Ethernet: Neuromorphic processors must communicate effectively with existing vehicle networks:- Real-time communication protocols
- Deterministic message timing
- Network load optimization
- Legacy system compatibility
AUTOSAR Compatibility: Integration with automotive software architectures requires:
- Standardized software interfaces
- Real-time operating system support
- Diagnostic and calibration protocols
- Over-the-air update capabilities
Development and Deployment Tools
Neuromorphic Development Environments
Simulation Platforms: Automotive engineers can develop and test neuromorphic algorithms using:- Hardware-accurate simulation environments
- Real-time sensor data playback capabilities
- Performance profiling and optimization tools
- Safety analysis and verification utilities
Hardware-in-the-Loop Testing: Neuromorphic systems integrate with existing HIL test environments:
- Real sensor integration for algorithm validation
- Scenario-based testing with repeatable conditions
- Performance benchmarking against traditional systems
- Regulatory compliance testing
Algorithm Development
Transfer Learning: Existing AI models can be converted to neuromorphic implementations:- CNN-to-SNN conversion tools
- Performance optimization for neuromorphic hardware
- Accuracy validation and calibration
- Deployment pipeline automation
Custom Algorithm Development: Engineers can develop specialized neuromorphic algorithms:
- Domain-specific programming languages
- Visual development environments
- Debugging and profiling tools
- Hardware resource optimization
Performance and Benefits
Power Efficiency Gains
Comparative Analysis: Neuromorphic systems demonstrate significant power advantages:- Traditional GPU-based ADAS: 200-500W
- High-performance neuromorphic system: 5-15W
- 90-95% power reduction for equivalent functionality
Battery Life Impact: For electric vehicles, neuromorphic processing extends range:
- Reduced computational power draw
- Lower cooling system requirements
- Extended sensor operation during parking
- Always-on security monitoring
Processing Performance
Latency Improvements: Neuromorphic systems achieve superior real-time performance:- Sub-millisecond response to sensor changes
- No computational pipeline delays
- Instant adaptation to changing conditions
- Elimination of batch processing bottlenecks
Throughput Advantages: Event-driven processing handles more sensor streams:
- Simultaneous processing of multiple camera feeds
- Real-time LiDAR and radar integration
- Continuous learning without performance impact
- Scalable processing based on scene complexity
Adaptability and Learning
Continuous Improvement: Neuromorphic systems learn and adapt during operation:- Driver behavior learning for personalized assistance
- Road condition adaptation for optimal performance
- Weather pattern recognition for enhanced safety
- Traffic pattern learning for efficient routing
Fleet Learning: Multiple vehicles can share learned behaviors:
- Distributed learning across vehicle fleets
- Rapid deployment of new capabilities
- Collective intelligence for improved safety
- Privacy-preserving learning protocols
Industry Adoption and Timeline
Current Deployment Status
Tier 1 Supplier Integration: Major automotive suppliers are developing neuromorphic solutions:- Bosch: Advanced driver assistance system integration
- Continental: Sensor processing and fusion applications
- Aptiv: Autonomous driving compute platforms
- ZF: Integrated safety system development
OEM Pilot Programs: Automotive manufacturers are evaluating neuromorphic technology:
- Mercedes-Benz: Advanced ADAS development
- BMW: Autonomous driving research programs
- Ford: Edge computing optimization projects
- General Motors: Next-generation vehicle platforms
Technology Maturity Timeline
2024-2025: Early Adoption- Specialized ADAS applications
- Sensor processing acceleration
- Development tool maturation
- Regulatory framework development
2026-2028: Mainstream Integration
- Level 3 autonomous driving systems
- Comprehensive sensor fusion platforms
- Automotive-grade neuromorphic processors
- Industry standard development
2029-2032: Widespread Deployment
- Level 4/5 autonomous vehicle enablement
- Next-generation vehicle architectures
- Advanced AI-driven vehicle features
- Global regulatory acceptance
Challenges and Considerations
Technical Challenges
Algorithm Development Complexity: Neuromorphic programming requires new approaches:- Event-driven programming paradigms
- Temporal dynamics understanding
- Hardware-software co-design
- Performance optimization techniques
Verification and Validation: Ensuring neuromorphic system reliability:
- Non-deterministic behavior analysis
- Safety case development for adaptive systems
- Testing coverage for learning algorithms
- Long-term stability validation
Market Considerations
Cost Structure: Initial neuromorphic implementations may carry premium costs:- Early-stage technology pricing
- Development cost amortization
- Specialized manufacturing requirements
- Supply chain establishment
Industry Standardization: Neuromorphic technology requires standard development:
- Interface specifications
- Safety certification processes
- Testing methodologies
- Regulatory frameworks
Future Implications
Technology Evolution
Advanced Neuromorphic Architectures: Next-generation systems will provide:- Higher neuron density and connectivity
- Improved learning algorithms and adaptation
- Better integration with automotive systems
- Enhanced safety and reliability features
Hybrid Processing Systems: Future vehicles may combine multiple processing approaches:
- Neuromorphic for real-time sensor processing
- Traditional processors for non-real-time tasks
- Quantum processors for optimization problems
- Optical processors for high-bandwidth applications
Industry Transformation
New Business Models: Neuromorphic computing enables innovative approaches:- Continuous learning-based service offerings
- Adaptive vehicle behavior customization
- Fleet intelligence and optimization services
- Real-time safety and efficiency improvements
Competitive Advantages: Early adopters may gain significant advantages:
- Superior real-time performance
- Enhanced energy efficiency
- Advanced safety capabilities
- Continuous improvement and adaptation
Resources & further readings:
1. Loihi: A Neuromorphic Manycore Processor with On-Chip Learning
https://ieeexplore.ieee.org/document/82594232. Neuromorphic computing using non-volatile memory
https://www.science.org/doi/10.1126/science.abj99793. Event-based vision for autonomous driving: A paradigm shift for bio-inspired visual sensing and perception
https://www.nature.com/articles/s42256-021-00422-24. A Survey of Neuromorphic Computing and Neural Networks in Hardware
https://dl.acm.org/doi/10.1145/3109859.31098785. Neuromorphic Computing for Safety-Critical Systems in Automotive Applications
https://ieeexplore.ieee.org/document/101064456. Benchmarking Keyword Spotting Efficiency on Neuromorphic Hardware
https://arxiv.org/abs/2408.16096The Mobility Chronicles
The Mobility Chronicles
3,497 followers
dariybogdan61
Emerged
BEST BITCOIN RECOVERY EXPERT; ETH AND USDT RECOVERY EXPERT HIRE. META TECH RECOVERY PRO
Bitcoin, USDT, USDC, and Ethereum ETC currently function within a regulatory uncertainty across numerous nations. Globally, governments are still formulating cryptocurrency regulations, which could potentially alter the legal framework. Furthermore, there exists a possibility of regulatory actions, prohibitions, or limitations that could affect Bitcoin's acceptance and valuation. A significant risk associated with Bitcoin investments is its inherent volatility. Its price can rapidly change, potentially resulting in substantial profits or losses. A high risk tolerance and preparedness for price fluctuations are essential for those considering crypto investments. The Bitcoin market is influenced by various factors, including supply and demand, regulatory changes, investor attitudes, and macroeconomic trends. Staying informed about the latest cryptocurrency news and developments is crucial for understanding market conditions that could affect Bitcoin's price. Crypto's value has been marked by significant fluctuations. Its price has experienced extreme volatility, reaching both unprecedented highs and lows. This volatility has attracted speculators and traders aiming to capitalize on these price movements. However, past performance does not guarantee future results, and Bitcoin investments carry inherent risks. The cryptocurrency landscape is constantly evolving, necessitating continuous updates on news and events. Subscribing to reputable newsletters, following industry influencers, and participating in online communities are effective ways to stay informed. Staying informed allows for adapting investment strategies based on market developments and making more accurate predictions. Thorough research is essential for successful cryptocurrency investments. Utilizing resources like META TECH RECOVERY PRO, identifying credible information sources, analyzing data, and staying current with industry news enables informed investment decisions. Studying real-world examples of both successes and failures provides valuable insights for navigating the dynamic world of crypto investments. With research as a key component, BTC investing can be approached confidently, increasing the likelihood of positive outcomes. In summary, investing in Bitcoin can be lucrative, but it requires careful market analysis, technological understanding, and risk assessment. META TECH RECOVERY PRO provides valuable resources for investors to conduct thorough research and make well-informed decisions before investing, and also to be able to recover their funds if they fall into a fraudulent investment scheme. For further information,
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Thank you.
Bitcoin, USDT, USDC, and Ethereum ETC currently function within a regulatory uncertainty across numerous nations. Globally, governments are still formulating cryptocurrency regulations, which could potentially alter the legal framework. Furthermore, there exists a possibility of regulatory actions, prohibitions, or limitations that could affect Bitcoin's acceptance and valuation. A significant risk associated with Bitcoin investments is its inherent volatility. Its price can rapidly change, potentially resulting in substantial profits or losses. A high risk tolerance and preparedness for price fluctuations are essential for those considering crypto investments. The Bitcoin market is influenced by various factors, including supply and demand, regulatory changes, investor attitudes, and macroeconomic trends. Staying informed about the latest cryptocurrency news and developments is crucial for understanding market conditions that could affect Bitcoin's price. Crypto's value has been marked by significant fluctuations. Its price has experienced extreme volatility, reaching both unprecedented highs and lows. This volatility has attracted speculators and traders aiming to capitalize on these price movements. However, past performance does not guarantee future results, and Bitcoin investments carry inherent risks. The cryptocurrency landscape is constantly evolving, necessitating continuous updates on news and events. Subscribing to reputable newsletters, following industry influencers, and participating in online communities are effective ways to stay informed. Staying informed allows for adapting investment strategies based on market developments and making more accurate predictions. Thorough research is essential for successful cryptocurrency investments. Utilizing resources like META TECH RECOVERY PRO, identifying credible information sources, analyzing data, and staying current with industry news enables informed investment decisions. Studying real-world examples of both successes and failures provides valuable insights for navigating the dynamic world of crypto investments. With research as a key component, BTC investing can be approached confidently, increasing the likelihood of positive outcomes. In summary, investing in Bitcoin can be lucrative, but it requires careful market analysis, technological understanding, and risk assessment. META TECH RECOVERY PRO provides valuable resources for investors to conduct thorough research and make well-informed decisions before investing, and also to be able to recover their funds if they fall into a fraudulent investment scheme. For further information,
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Thank you.
DingoBorat
Slim
There's plenty about learning and self learning etc in the description, which is lengthy.. (did not read all).Good evening from Perth...
It appears our old mates at Accenture Global Solutions Ltd have had a new Patent published just 3 weeks ago, naming us alongside Intel, but this time finally stating a true fact, Intel's Loihi is the research chip while we are the actual commercial chip, nice, finally
Maybe Dio or the crew have already posted this newish patent, but here is the link anyway.... when the f... will a company sign an IP contract...moan,
moan
...
That's just some of it..
I know Intel claims some kind of learning abilities but apart from the fact that they "aren't" yet commercial with it, what this patent is describing sounds more like our particular ball game @Diogenese?
I wonder how far off Loihi 3 is and what it's capabilities will be?..
Edit... Came out June 8th, did we know this?
..Intel Loihi 3 Chip: A Game-Changer in Neuromorphic Computing
Intel's Loihi 3 chip mimics the human brain to deliver 100x energy efficiency for AI tasks, marking a major leap in sustainable, edge-based artificial intelligence.
trainthealgo.com
DingoBorat
Slim
From the article..There's plenty about learning and self learning etc in the description, which is lengthy.. (did not read all).
View attachment 89891
View attachment 89892
View attachment 89893
...
That's just some of it..
I know Intel claims some kind of learning abilities but apart from the fact that they "aren't" yet commercial with it, what this patent is describing sounds more like our particular ball game @Diogenese?
I wonder how far off Loihi 3 is and what it's capabilities will be?..
Edit... Came out June 8th, did we know this?
Intel Loihi 3 Chip: A Game-Changer in Neuromorphic Computing
Intel's Loihi 3 chip mimics the human brain to deliver 100x energy efficiency for AI tasks, marking a major leap in sustainable, edge-based artificial intelligence.trainthealgo.com
"The chip packs 1.15 billion artificial neurons and 128 billion synapses, a massive leap from its predecessor, Loihi 2"(which had 128,000 neurons and 100 million synapses).
The full AKD1000 has I think up to 1.2 million neurons and 100 billion synapses?
So Loihi 3 is up over 25% on connections, which is the more important number (but probably not directly comparable on just those measures).
And "AKIDA 2.0 IP" has the extra advantages that TENNs supplies.
"Intel isn't alone in this space. IBM's TrueNorth and BrainChip's Akida are also pushing neuromorphic boundaries. But Loihi 3's scale, efficiency, and developer support give it a strong edge. Intel plans to roll out commercial applications by Q3 2026, targeting sectors like healthcare, autonomous vehicles, and industrial automation"
There's plenty of talk in the article about learning abilities as well..
DingoBorat
Slim
Okay, it's odd that that is the only article "I" could find on the Net in relation to Loihi 3..
But I did find this, from April last year and actually from Intel..
"Hala Point packages 1,152 Loihi 2 processors produced on Intel 4 process node in a six-rack-unit data center chassis the size of a microwave oven. The system supports up to 1.15 billion neurons and 128 billion synapses distributed over 140,544 neuromorphic processing cores, consuming a maximum of 2,600 watts of power. It also includes over 2,300 embedded x86 processors for ancillary computations"
The numbers seem familiar
..Somebody tell me what the frack is going on....
The previous article looks like it's..
So this "Hala Point" is the size of a microwave oven and is already in 4nm?!
It ain't getting smaller any time soon, is my guess...
Last edited:
Frangipani
Top 20
From ARM's partner ecosystem catalog found by FF
View attachment 89857
BrainChip
Ubiquitous, distributed AI.<br/>Close to the sensor.<br/>Inspired by the human brain. <br/>www.arm.com
Well, it appears that FF hasn’t checked the relevant ARM ecosystem partner webpage since at least 14 December 2024, see the archived version below.
Content-wise, nothing has been updated since, as far as I can tell; there have merely been some negligible changes in layout such as a different font size, font colour, background colour…
Please compare: This is what the “BrainChip’s Akida IP Solution Brief” and “About BrainChip” sections already looked like at the time, more than eight months ago:
BrainChip
Ubiquitous, distributed AI.<br/>Close to the sensor.<br/>Inspired by the human brain. <br/>
web.archive.org
And the rest of the webpage content - save some additional minor layout changes as well as staff updates - has actually been unchanged since at least 9 December 2023 (!), which was the first time ever this specific webpage got archived on The Wayback Machine, including both the image relating to the Mercedes-Benz Vision EQXX collaboration (image 2 of 18) and the info that “BrainChip’s Akida IP is fully compatible with Arm’s product families” (updated version since at least mid-December 2024, see above: “BrainChip’s Akida event-driven compute IP is fully compatible with Arm’s product families”. For some reason, the second half of the second sentence - “even those in locations where there is limited or no connectivity.” - was dropped).
BrainChip
Ubiquitous, distributed AI.<br/>Close to the sensor.<br/>Inspired by the human brain. <br/>
web.archive.org
So nothing new to see here.
By the way, that photo showing a gentleman behind the wheel of a Mercedes-Benz is not a promotional image supplied by the German carmaker, in case anyone wondered.
It is simply a stock photo published on Unsplash in March 2021, created by Fortune Vieyra from California and can be found all over the internet when you do a Google image search.
Attachments
BrainShit
Regular
From ARM's partner ecosystem catalog found by FF
View attachment 89857BrainChip
Ubiquitous, distributed AI.<br/>Close to the sensor.<br/>Inspired by the human brain. <br/>
Holy crab... it took him 3 years for discovery!
BrainShit
Regular
Well, it appears that FF hasn’t checked the relevant ARM ecosystem partner webpage since at least 14 December 2024, see the archived version below.
Content-wise, nothing has been updated since, as far as I can tell; there have merely been some negligible changes in layout such as a different font size, font colour, background colour…
Please compare: This is what the “BrainChip’s Akida IP Solution Brief” and “About BrainChip” sections already looked like at the time, more than eight months ago:
BrainChip
Ubiquitous, distributed AI.<br/>Close to the sensor.<br/>Inspired by the human brain. <br/>
View attachment 89897
View attachment 89881
And the rest of the webpage content - save some additional minor layout changes as well as staff updates - has actually been unchanged since at least 9 December 2023 (!), which was the first time ever this specific webpage got archived on The Wayback Machine, including both the image relating to the Mercedes-Benz Vision EQXX collaboration (image 2 of 18) and the info that “BrainChip’s Akida IP is fully compatible with Arm’s product families” (updated version since at least mid-December 2024, see above: “BrainChip’s Akida event-driven compute IP is fully compatible with Arm’s product families”. For some reason, the second half of the second sentence - “even those in locations where there is limited or no connectivity.” - was dropped).
BrainChip
Ubiquitous, distributed AI.<br/>Close to the sensor.<br/>Inspired by the human brain. <br/>
View attachment 89882 View attachment 89883
So nothing new to see here.
By the way, that photo showing a gentleman behind the wheel of a Mercedes-Benz is not a promotional image supplied by the German carmaker, in case anyone wondered.
It is simply a stock photo published on Unsplash in March 2021, created by Fortune Vieyra from California and can be found all over the internet when you do a Google image search.
View attachment 89884 View attachment 89885
Well, well.... I assume the ARM page was build in 2022/05/XX.
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