BRN Discussion Ongoing

[CHIPS]

#semiconductors #aichips #npu #tpu #gpu #asic #inferentia #mtia #tsmc #chipdesign #edgeai #datacenterai #testflow | Ali Kamaly | 11 comments

What’s Inside an AI Chip? — A Visual Breakdown AI isn’t powered by just GPUs anymore. It’s a symphony of specialized processors, memory blocks, and interconnects—each tailored to speed up different stages of AI workloads. Here’s what goes inside a modern AI chip: → GPU / TPU / NPU / FPU / CPU...

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[Victor Lee]

Chevron is currently the only major American oil company operating in Venezuela. If the United States continues to advance its plan to revitalize Venezuela's oil industry, it is very likely to give the company a huge advantage. The development of the situation in the coming months and how all parties respond will have a significant impact on the final outcome. Most of us know that the United States has made many missteps in its foreign policy, many of which are related to regime changes. But for now, the situation seems quite optimistic.
Chevron also has oil and gas production rights in Guyana, which may be temporarily put on hold at present. But in the future, this might become a major problem.
If you want to get more stock investment advice, please contact me: https://wa.me/61410871340

 

[Frangipani]

https://www.sciencedirect.com/science/article/abs/pii/S2542660525003762

Internet of Things​

Available online 29 December 2025, 101862

Neuromorphic Solar Edge AI for Sustainable Wildfire Detection​

Author
Raúl Parada
Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Castelldefels, 08860, Catalonia, Spain
Available online 29 December 2025.


Cite
https://doi.org/10.1016/j.iot.2025.101862
Get rights and content

Highlights​

• •
  Neuromorphic edge AI drones achieve 87% solar autonomy for wildfire detection
• •
  BrainChip Akida enables 4,200 patrol hours/year, 3× longer than CPU-based systems
• •
  Fleet scaling reduces detection latency from 18 hours (1 drone) to 2.2 hours (8 drones)

​

Abstract​

This paper presents a feasibility study of a solar-autonomous wildfire detection system using neuromorphic edge AI on fixed-wing drones. Through a comprehensive year-long simulation over Parc del Garraf (Catalonia), we evaluate three edge computing platforms, Raspberry Pi 4, Google Coral TPU, and BrainChip Akida, integrated into solar-optimized eBee X drones.

Results show that the BrainChip Akida achieves 4,200 patrol hours per year, nearly three times that of traditional CPU systems, while maintaining 87% solar energy autonomy. The Google Coral TPU and Raspberry Pi 4 reach 66% and 52% autonomy, respectively. Fleet scaling analysis demonstrates that increasing drone count from one to eight reduces median wildfire detection time from 18 to 2.2 hours, surpassing critical response thresholds. Seasonal analysis reveals Akida-based systems can operate fully on solar energy during summer and most of spring and fall, minimizing grid dependency. These findings establish neuromorphic computing as a foundational technology for sustainable, perpetual environmental monitoring within the Internet of Robotic Things (IoRT).

​

Graphical abstract​

1. Download: Download high-res image (296KB)
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Introduction​

Wildfires are now responsible for billions of dollars in annual economic losses and hundreds of lives globally. In the Mediterranean basin, over 500,000 hectares are burned yearly, often with delayed detection times exceeding 12 hours. These delays critically hinder suppression efforts and exacerbate damage. As climate change intensifies droughts and heatwaves, the urgency for faster, autonomous detection systems grows [1]. Mediterranean ecosystems, in particular, face growing risk as traditional fire detection systems, relying on satellite imagery, human patrols, or fixed ground sensors, struggle with temporal latency, limited spatial coverage, and infrastructure dependency. Other proposals incorporate artificial intelligence (AI) and fifth generation (5G) for wildfire control [2], but such approaches still rely heavily on communication infrastructure.

Recent advances in unmanned aerial vehicles (UAVs) have opened new possibilities for continuous, real-time environmental monitoring. However, most current drone-based detection systems are hindered by significant energy limitations, requiring frequent manual recharging or access to grid-based infrastructure. This severely restricts their autonomy and scalability, especially in remote or high-risk natural environments where rapid response is critical.

The Internet of robotic things (IoRT) envisions fully autonomous, intelligent agents operating over long durations without human intervention. Achieving this vision in the context of wildfire monitoring demands breakthroughs in two main areas: (i) onboard decision-making through efficient Edge AI [3], and (ii) self-sustaining energy systems. While Edge AI reduces reliance on cloud connectivity and improves latency, traditional computing platforms such as CPUs or even typical TPUs consume too much power for extended operation. As such, they cannot meet the demands of uninterrupted surveillance missions without significant energy support.

Neuromorphic computing offers a compelling solution. By mimicking the efficiency of biological brains [4], neuromorphic processors such as BrainChip Akida enable ultra-low-power inference, operating at a fraction of the energy cost of conventional architectures. Coupled with solar energy harvesting, this opens the door to continuous aerial monitoring, without external energy input, for the first time to the best of our knowledge.

This work addresses the critical challenge of enabling truly autonomous environmental monitoring drones by combining neuromorphic Edge AI and solar energy systems. The core objectives of our study are:

• 1.
  To quantify and compare the energy sustainability potential of neuromorphic and traditional edge computing platforms when deployed on UAVs in realistic wildfire surveillance scenarios.
• 2.
  To simulate year-round operations using real solar irradiance and environmental data, modelling energy harvesting and consumption dynamics in detail.
• 3.
  To establish practical benchmarks for operational availability, solar autonomy, and wildfire detection performance across different hardware configurations.

The rest of this paper is structured as follows: Section 2 reviews related work in UAV-based wildfire detection and sustainable edge AI. Section 3 details the simulation framework, hardware platforms, energy modeling, and fleet scaling strategies. Section 4 presents and analyzes the simulation results, including mission time allocation, solar autonomy, seasonal variations, and cost-effectiveness. Section 5 discusses the broader implications of these results for sustainable IoRT systems. Finally, Section 6 concludes the paper and outlines future research directions.

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Section snippets​

Related Work​

Recent years have witnessed significant progress in UAV-based wildfire detection, AI-enabled edge processing, and neuromorphic computing for environmental monitoring. Here, we provide a structured comparison of leading approaches and clarify the main research gaps that remain. …

​

Methodology​

This section presents the complete simulation methodology designed to evaluate the energy autonomy, detection capacity, and mission viability of solar-powered drones equipped with different edge AI platforms, including neuromorphic computing. The simulation is implemented using Python and is verifiable via the accompanying codebase. It models the performance of drones operating autonomously over a full year, incorporating solar harvesting dynamics, energy consumption profiles, patrol logic, and…

​

Results and Analysis​

This section presents the key simulation outcomes comparing the Raspberry Pi 4, Google Coral TPU, and BrainChip Akida. The results show that the Akida platform achieves 87% solar autonomy and 4,200 patrol hours per year—three times that of CPU-based systems. Analysis includes patrol time distribution, detection efficiency, seasonal performance, and cost-effectiveness. A fleet scaling study shows exponential reductions in wildfire detection latency with increasing drone count, achieving optimal…

​

Discussion and Implications​

This section reflects on the broader implications of the simulation results, emphasizing the technological, environmental, and operational significance of solar-autonomous UAV systems. By analyzing how neuromorphic computing shifts the boundaries of edge AI deployment, we explore how energy efficiency, fleet scalability, and seasonal synergy contribute to a new paradigm in sustainable wildfire monitoring. Key findings are contextualized within the Internet of Robotic Things (IoRT) framework,…

Conclusions​

This research demonstrates the first viable solar-autonomous wildfire detection system using neuromorphic edge AI, achieving 87% energy self-sufficiency through breakthrough power efficiency improvements. The comprehensive year-long simulation reveals that hardware architecture selection—specifically neuromorphic versus traditional computing—enables qualitatively different operational capabilities in autonomous systems. Key findings include a neuromorphic computing system enabling practical…

CRediT authorship contribution statement​

Raúl Parada: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. …

Declaration of competing interest​

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. …

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  Edge computing driven forest fire spread simulation: An energy-aware study​

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  SNN-BERT: Training-efficient Spiking Neural Networks for energy-efficient BERT​

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View more references

NeuromorphicCore.AI aka Bradley Susser wrote a blog post (my bold below) about Raúl Parada’s journal article “Neuromorphic Solar Edge AI for Sustainable Wildfire Detection” that I shared here last week :

Neuromorphic Solar Drones Reach 87% Energy Autonomy in Mediterranean Wildfire Monitoring Simulations - NeuromorphicCore.ai | NeuromorphicCore.AI

𝗡𝗲𝘂𝗿𝗼𝗺𝗼𝗿𝗽𝗵𝗶𝗰 𝗦𝗼𝗹𝗮𝗿 𝗗𝗿𝗼𝗻𝗲𝘀 𝗔𝗰𝗵𝗶𝗲𝘃𝗲 𝟴𝟳% 𝗘𝗻𝗲𝗿𝗴𝘆 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆 𝗳𝗼𝗿 𝗘𝗮𝗿𝗹𝘆 𝗪𝗶𝗹𝗱𝗳𝗶𝗿𝗲 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 What if wildfire detection drones could fly almost year-round without plugging in? New research shows solar-powered drones running BrainChip’s Akida neuromorphic processor reach 87% energy autonomy—delivering 3×...

www.linkedin.com

Neuromorphic Solar Drones Reach 87% Energy Autonomy in Mediterranean Wildfire Monitoring Simulations - NeuromorphicCore.ai

A feasibility study evaluates solar-powered drones using neuromorphic edge AI for year-round wildfire monitoring in Mediterranean environments.

neuromorphiccore.ai

Neuromorphic Solar Drones Reach 87% Energy Autonomy in Mediterranean Wildfire Monitoring Simulations​

neuromorphiccore January 5, 2026
0 Comments

Wildfires across the Mediterranean basin consume over 500,000 hectares annually, with detection delays often exceeding 12 hours. In regions experiencing such postponements, fire spread multiplies exponentially, threatening wildlife corridors and displacing thousands of residents each year. These delays critically undermine suppression efforts, amplifying destruction as climate-driven droughts and heatwaves heighten fire risk across the region. Faced with this escalating threat, researchers are validating autonomous methods that can detect ignition events before they grow beyond containment thresholds.

A new feasibility study from the Centre Tecnològic de Telecomunicacions de Catalunya demonstrates how solar-autonomous drones equipped with neuromorphic edge AI can monitor wildfire-prone regions nearly continuously throughout the year. Through comprehensive year-long simulations over Parc del Garraf in Catalonia, researchers evaluated three edge computing platforms integrated into solar-optimized eBee X fixed-wing drones under realistic atmospheric conditions with mean solar irradiance of 235 W/m², representative of regional conditions.

The results reveal that the BrainChip Akida neuromorphic processor achieved 4,200 patrol hours per year while maintaining 87% solar energy autonomy, representing nearly three times the patrol time of the CPU-based platform evaluated. In comparison, the Google Coral TPU and Raspberry Pi 4 reached 66% and 52% autonomy respectively.

How Spiking Neural Networks Excel at Fire Detection​

Hardware results alone reveal performance gaps, but understanding their origin requires examining how neuromorphic computation processes sensory data differently. Rather than processing every image in full, neuromorphic systems react only to visual change, which is common in natural scenes but rare in ignition events. The Akida platform’s exceptional performance stems from three core mechanisms particularly suited to wildfire surveillance, each contributing to lower energy use and reduced latency for wildfire-relevant visual patterns.

First, event-driven computation activates only when input features change substantially. For aerial monitoring where most frames contain uniform forest canopy, this sparse activation dramatically reduces computation while accelerating response. Only pixels exhibiting temporal changes like smoke plumes or flame reflections trigger neuronal spikes, often reducing active neuron count by large margins relative to frame-based processing. This approach resembles how mammalian visual cortex cells respond selectively to movement and contrast changes rather than processing entire visual fields continuously.

Second, temporal sparsity enables the system to encode information in spike timing rather than activation magnitudes, delivering low-latency detection at reduced power draw. Wildfire signatures such as flickering flames and rising smoke are naturally captured without expensive recurrent memory structures. A smoke plume’s characteristic upward drift or flame flicker at 2–10 Hz is encoded in spike intervals, enabling robust detection with approximately 10 millisecond latency while consuming minimal energy.

Third, neuromorphic memory integration tightly couples on-chip SRAM with spiking neuron arrays, eliminating off-chip memory access that typically drains energy in conventional architectures. Wildfire detection models fit entirely on-chip at roughly 2 megabytes, avoiding external DRAM and PCIe transfers that dominate standard processor energy budgets.

Energy Consumption and Mission Performance​

The simulation modeled daily operational cycles of 10 hours active patrol and 14 hours idle time across 365 days. Active patrol includes continuous scanning and onboard processing, while idle periods encompass charging and standby states without passive monitoring. Mission utilization represents the proportion of annual time (out of 8,760 total hours) that drones actively patrol, excluding communication overhead, which operates concurrently.

Energy consumption calculations revealed stark contrasts between platforms. The Raspberry Pi 4 consumed 412 watt-hours per day, the Google Coral TPU required 352.4 watt-hours daily, while the BrainChip Akida needed only 315.1 watt-hours per day. This 23.5% reduction compared to CPU-based systems translates to approximately 35 kilowatt-hours saved annually per drone, equivalent to roughly 180 fewer battery charge cycles per year and reduced thermal management requirements.

From a practical standpoint, these energy savings directly determine surveillance capability. The Akida platform achieved 48% active patrol fraction with drones patrolling 4,200 hours annually. The Coral TPU reached 35% with 3,100 patrol hours, while the Raspberry Pi 4 managed only 16% at 1,400 patrol hours per year. Expressed as cost per active surveillance day, the neuromorphic system delivers monitoring at substantially lower operational cost despite higher initial hardware investment.

Beyond single-drone evaluations, scaling analysis shows how fleet size impacts detection responsiveness. Expanding the fleet rapidly increases the pace of wildfire detection. A single drone achieved 18-hour median detection time, falling well above critical response thresholds. Increasing to four drones reduced detection time to 4.5 hours, successfully meeting critical response requirements. An eight-drone fleet achieved 2.2-hour detection time, enabling earlier fire suppression deployment.

Fleet Coordination and Coverage Optimization​

The fleet coordination framework implements several strategies selected for reduced energy expenditure and coverage completeness. Voronoi-based partitioning dynamically distributes search areas, reducing overlap and extending patrol reach by assigning each UAV to the spatial region closest to its current position. Cell boundaries update every patrol cycle based on UAV positions and remaining battery levels, ensuring balanced workload distribution even with varying energy states across the fleet.

Coverage optimization uses modified path planning algorithms with boustrophedon patterns, selected to minimize turn maneuvers that consume extra energy in fixed-wing aircraft. Route density is modulated by fire risk heatmaps derived from historical data, with higher-risk zones receiving proportionally more revisits.

When a UAV detects potential wildfire with confidence exceeding 85%, it immediately broadcasts an alert packet containing GPS coordinates via low-power LoRa radio with kilometer-scale range under line-of-sight conditions. Neighboring UAVs within communication range temporarily exclude a 500-meter radius around the detection point from their patrol routes, preventing duplicate alarms and wasted surveillance effort.

Seasonal Performance and Year-Round Operation​

Monthly sustainability analysis reveals crucial insights for deployment planning in Mediterranean climates where average solar irradiance varies from 85 W/m² in December to 385 W/m² in June. The Akida platform demonstrated 100% solar-sustainable days during summer months from June through August, enabling continuous autonomous operation. Shoulder seasons from April through May and September through October showed 80–95% sustainable days requiring minimal grid charging.

Even during winter months from December through February, Akida systems maintained 25–35% sustainable days, necessitating regular charging but still contributing substantial solar energy. In practical terms, this seasonal pattern aligns optimally with wildfire risk profiles in the Mediterranean, where fire danger peaks during the same summer months when solar energy availability reaches maximum levels, naturally aligning energy supply with operational demand.

The Google Coral TPU showed viable seasonal operation with 95–100% sustainability during peak summer and 55–95% sustainability from March through October. The Raspberry Pi 4 demonstrated more constrained performance with maximum 90% sustainability even during optimal conditions and only four months exceeding 60% sustainability.

Economic Performance and Operational Tradeoffs​

Operational savings drive the economic case for neuromorphic platforms despite higher upfront investment. Over three years, reduced charging infrastructure requirements and extended patrol capabilities offset initial hardware costs. When derived from patrol hours and modeled detection frequency, the neuromorphic system yields approximately 2 detection events per dollar of total cost of ownership when accounting for hardware, energy consumption, and maintenance expenses.

With 840 annual detections compared to 280 for the Pi 4 and 620 for the Coral TPU, the neuromorphic system provides substantially greater monitoring coverage. When expressed as cost per active surveillance day, the Akida platform at $249 hardware cost achieves approximately $0.15 per patrol hour over three years, compared to $0.20 for Coral and $0.26 for Pi 4 when factoring in reduced operational availability and higher energy costs for conventional processors.

Improving Winter Energy Sustainment​

Maintaining drone operability during winter requires energy diversification rather than increased capacity. The winter energy situation where Akida systems maintain 25–35% solar-only days can be managed through three complementary hybrid energy strategies, each with defined feasibility characteristics.

Small-scale vertical-axis wind turbines suitable for UAV integration at 50–100 grams with 5 watt nominal output can provide supplementary power during winter when wind speeds typically increase in Mediterranean regions. Preliminary analysis suggests this approach could increase winter autonomy from 30% to 55% of days, with the additional 80-gram mass penalty reducing flight endurance by approximately 8 minutes per cycle while extending overall operational availability. This technology currently sits at readiness level 6, with laboratory prototypes validated in relevant environments.

Thermoelectric generator integration enables waste heat recovery from battery discharge cycles and electronics using lightweight TEG modules mounted on battery enclosures. Although power output remains modest at 0.5–1.5 watts, this passive energy recovery can reduce idle power consumption by 30–50%, particularly valuable during overnight periods. TEG systems add minimal weight below 30 grams and require no moving parts, enhancing system reliability. Implementation is feasible within current design constraints and could deliver 5–8% improvement in annual operational uptime.

Deploying solar-augmented charging stations at intervals of 20–30 kilometers provides winter energy backup while maintaining infrastructure minimalism. Stations powered by ground-based solar arrays of 1–2 square meters with battery storage of 500–1,000 watt-hours can support multiple UAVs with minimal grid dependence below 10% annual energy draw. This approach enables 90% or greater winter autonomy and represents the most mature adaptation strategy, deployable within 12–18 months in pilot regions.

Future Directions and Broader Applications​

The modeling framework extends beyond wildfire detection to various autonomous environmental monitoring operations including marine pollution tracking, biodiversity observation, and atmospheric data collection where energy independence and edge intelligence remain critical. Advances from cooperative robotics research and low-power neuromorphic imaging may integrate with this framework to support precision agriculture monitoring or habitat assessment in protected areas.

Future research directions include advanced energy-aware management systems incorporating weather prediction and adaptive mission planning, multi-drone cooperative energy sharing for extended swarm operation, integration of multiple renewable energy sources to achieve complete energy autonomy, and real-world validation through field deployment. Field trials scheduled for Mediterranean ecosystems could influence regulatory frameworks for autonomous environmental monitoring and inform conservation policy by demonstrating operational feasibility of perpetual surveillance systems in protected natural areas.

By grounding neuromorphic computing in real-world drone operations, this work advances sustainable robotic monitoring and signals a transition toward perpetual autonomous observation across environmental systems.

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Citation: Parada, R. (2025). Neuromorphic Solar Edge AI for Sustainable Wildfire Detection. Internet of Things, IOT 101862. https://doi.org/10.1016/j.iot.2025.101862

 

[itsol4605]

On-Device AI kills DataCenters

#aievolution #businessstrategy #edgecomputing #clevel | Christian Schmidt | 13 comments

Data centers? Dead weight if Aravind Srinivas is right. Perplexity's CEO predicts phone AI buries them. On-device AI kills data centers. Perplexity CEO Aravind Srinivas nails it—cloud GPUs lose to phone chips. Tech giants like Meta and Google are spending $500B to $5T on GPU farms for AI...

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[CHIPS]

Listen to this video on LinkedIn. She is talking about AI at the edge ...
I don't know how to copy the video to this page here.

#ces2026 #intelcoreultra | Intel Corporation

It's almost time! #CES2026 begins today with the launch of #IntelCoreUltra Series 3, a processor reimagined with AI acceleration to change how you work. Follow along to see demonstrations, hear from executives, and catch up during our keynote livestream. No matter how you experience CES, get...

www.linkedin.com

 

[smoothsailing18]

Listen to this video on LinkedIn. She is talking about AI at the edge ...
I don't know how to copy the video to this page here.

#ces2026 #intelcoreultra | Intel Corporation

It's almost time! #CES2026 begins today with the launch of #IntelCoreUltra Series 3, a processor reimagined with AI acceleration to change how you work. Follow along to see demonstrations, hear from executives, and catch up during our keynote livestream. No matter how you experience CES, get...

www.linkedin.com

Unfortunately no neuromorphic compute I don't believe.

 

[Tezza]

Ive spent a couple of hours watching videos on ces 2026 and am yet to hear AI at the edge mentioned. Not sure 2026 will be our year.

 

[smoothsailing18]

Ive spent a couple of hours watching videos on ces 2026 and am yet to hear AI at the edge mentioned. Not sure 2026 will be our year.

It's starts tomorrow on the 6th so surely you will not know to many details until the doors open, imo. However the Intel video above your post is all-about.... egdeAi at CES this year.

 

[Gazzafish]

Frontgrade Gaisler going from strength to strength

https://www.satnow.com/news/details...-on-vaias-project-for-fault-tolerant-space-ai

Extract :-“
Rapidity Space and Frontgrade Gaisler are excited to announce their collaboration in the VAIAS project, a strategic initiative aimed at advancing energy-efficient, fault-tolerant AI computing for future space missions. The project is funded through the ESA Phi-Lab Sweden programme, led by RISE Research Institutes of Sweden, with contributions from ESA and Vinnova.

Central to the project is the Frontgrade Gaisler GR801, a next-generation processing platform that uniquely combines a radiation-tolerant NOEL-V RISC-V processor with BrainChip’s Akida neuromorphic processing engine, enabling reliable onboard inference at low power.”

 

[7für7]

Don’t forget guys…Price sensitive announcement any time from yesterday!

 

[HopalongPetrovski]

It’s still only just after 7pm Monday night in New York. They’re just having their din dins and settling in for the night. Don’t expect anything much till tomorrow and even then it’ll probably just be a podcast or two trying to whet some appetite's and unlikely to cause pandemonium in our market.

 

[manny100]

Our Archiles heel will eventually be our Moat - time from engagement to adoption to revenue.
The wait tests patience but it should be worth it.
Others that follow will have similar issues of timing in the cycle as they cannot copy our tech. The only thing in their favor is that by the time they start up Neuromorphic will be business/consumer full on.

 

[Pom down under]

Chevron is currently the only major American oil company operating in Venezuela. If the United States continues to advance its plan to revitalize Venezuela's oil industry, it is very likely to give the company a huge advantage. The development of the situation in the coming months and how all parties respond will have a significant impact on the final outcome. Most of us know that the United States has made many missteps in its foreign policy, many of which are related to regime changes. But for now, the situation seems quite optimistic.
Chevron also has oil and gas production rights in Guyana, which may be temporarily put on hold at present. But in the future, this might become a major problem.
If you want to get more stock investment advice, please contact me: https://wa.me/61410871340

 

[keyeat]

Nvidia unveils 'reasoning' AI technology for self-driving cars

Nvidia is partnering with Mercedes-Benz to launch a driverless car powered by its 'Alpamayo' tech

www.bbc.com

 

[smoothsailing18]

BrainChip is demonstrating Akida 2, an early preview of Akida 3 Architecture with TENNs LLM integration, ultra-low power connectivity with Bluetooth and WiFi for wearable visual classification, a visual computing pipeline for drones and mobile devices, and an Edge AI Cybersecurity model running on the Akida Edge AI Box

 

[SharesForBrekky]

Juniper Research Unveils Top 10 Emerging Tech Trends to Watch in 2026

Juniper Research today released its Top 10 Emerging Tech Trends for 2026; revealing how frontier technologies such as post-quantum cryptography, neuromorphic computing, and physical AI are expected to shape enterprise strategy and investment over the coming year.

www.juniperresearch.com

2. Neuromorphic Computing: Commercial Chipsets That Address AI Bottlenecks to Launch in 2026

Each trend is ranked by expected impact, with analysis explaining what will happen, why it is expected to happen, and why 2026 represents a critical point for adoption and deployment.

 

[TheDon]

Nvidia unveils 'reasoning' AI technology for self-driving cars

Nvidia is partnering with Mercedes-Benz to launch a driverless car powered by its 'Alpamayo' tech

www.bbc.com

I really hope that mercedes benz will adopt Akida first before the Chinese does. Otherwise they will end up like Nokia company.

My opinion only

TheDon

 

[smoothsailing18]

I really hope that mercedes benz will adopt Akida first before the Chinese does. Otherwise they will end up like Nokia company.

My opinion only

TheDon

That above seemes to have sidestepped BRN’s tech. Hopefully we are in their with Mercedes in another form.

 

[7für7]

It’s still only just after 7pm Monday night in New York. They’re just having their din dins and settling in for the night. Don’t expect anything much till tomorrow and even then it’ll probably just be a podcast or two trying to whet some appetite's and unlikely to cause pandemonium in our market.

Yes but some companies already announced their new tech… even Lego announced a new smart Lego product… so…

 

[7für7]

That above seemes to have sidestepped BRN’s tech. Hopefully we are in their with Mercedes in another form.

I already mentioned Long time ago that Benz would never implement a tech of an absolute newcomer for production ready vehicles… it’s too risky and also it’s not good for the image… implementing products from NVIDIA sounds more glamorous…. I don’t expect much