BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
wilzy123
Founding Member
Pom down under
Top 20
What if the whole time you are T&J from hotcrapper I’d think that would be more believableWhat if Rob was the whole time T&J from HC???what if he created a imaginary AI company and all this was a scam? What if it was a social project to find out how far people in a forum would go to praise a company even it’s not existing
(sarcasm off…just in case)
Fullmoonfever
Top 20
Pom down under
Top 20
Talking about holograms, one of my favourite programs back in the dayBugger....so he's not a cutting edge Akida AI hologram like Cortana in Halo
What if the whole HC and TSE forum is actually T&J but because of his schizophrenia, he don’t realise it? Maybe he lives in a matrix full of agents after the big fight he won. And I am neo!
supersonic001
Regular
Untether AI and J-Squared Unveil Groundbreaking AI-Enabled Rugged PC Chassis at AUSA - https://lnkd.in/gsTTCR9a | Chris Stevens
Untether AI and J-Squared Unveil Groundbreaking AI-Enabled Rugged PC Chassis at AUSA - https://lnkd.in/gsTTCR9a Untether AI J-Squared Technologies Inc.™
I guess we like this tooo ….. ! Thx Anil
NXP is in the Akida VVDn AI Edge Box
Tothemoon24
Top 20
Nvidia’s H100 AI #GPUs are taking the tech world by storm, but their reign comes at the price of a hefty energy bill.
According to a report from #CBInsights and #Stocklytics.com, these power-hungry processors are projected to consume a staggering 13,797 GWh in 2024, exceeding the annual energy consumption of nations like #Georgia and #CostaRica.
Imagine this, a legacy data center consumes 10 kW/rack where #CyrusOne, #KKR owned leading global data center operator and developer specialising in #AI applications, consumes 300 kW/rack!
But why do #GPUs consume so much power?
Data center #GPUs consume a substantial amount of power primarily due to their high computational requirements and the complex algorithms they handle. These #GPUs optimize parallel processing tasks like #machinelearning and #dataanalytics, involving simultaneous processing of vast amounts of data.
While #parallel processing speeds up data processing, one demerit is that, at a time most parts of a chip are active. This constant computation, coupled with the execution of complex algorithms, demands significant computational power, thereby increasing energy consumption.
The large-scale deployment of #GPUs in data centers, where racks and clusters utilize hundreds or thousands of #GPUs further amplify their collective power consumption. This combination of factors underscores the considerable energy consumption associated with data center GPUs.
Successfully navigating these challenges and fostering innovation will shape the future landscape of #AI computing.
So, what options do we have?
● #Amazon, frenemy to Nvidia, recently unveiled Arm based Graviton4 and Trainium2 chips holds promise for efficiency gains.
● In the near to medium term, #Neuromorphic computing is being researched aggressively as an alternative to synchronous parallel computing architectures. Neuromorphic computing is an asynchronous computing paradigm which runs on event based ‘spikes’ rather than a clock signal. And drastically lowers the power consumption.
● Big money is going into enabling tech like liquid cooling - #KKR acquired CoolIT Systems for $270 mn and Bosch acquired Jetcool through its venture arm
While CooIT Systems becomes the supplier for Cyrus One, #KKR makes money on both!
wilzy123
Founding Member
Reuben
Founding Member
As per investor relations, both are still working with brainchip... Nandan and Rob
jtardif999
Regular
Could they just be a recruitment firm?
Bravo
If ARM was an arm, BRN would be its biceps💪!
Ben Affleck ?
Shadow59
Regular
Sounds like an afflickionBen Affleck ?
Fullmoonfever
Top 20
Presuming already posted but too lazy to check search this time but if not, nice for ARM Design Engineer to acknowledge us in the mix...difference is out of that group, we can integrate both DVS & DAS from multiple sensors
www.linkedin.com
Kailash Prasad
+ Follow
Have you ever wondered how the human eye and ear
can process complex and dynamic scenes with such high speed and accuracy? Imagine if we could design artificial sensors that mimic the biological mechanisms of vision and hearing, and produce data that is more efficient and meaningful than conventional sensors.
In this post, I will introduce you to two types of neuromorphic sensors: the Dynamic Vision Sensor (DVS) and the Dynamic Audio Sensor (DAS).
These sensors are inspired by the structure and function of the retina and the cochlea, respectively, and use a novel paradigm of event-based sensing. Unlike conventional sensors that capture frames or samples at a fixed rate, event-based sensors only output data when there is a change in the input signal, such as brightness or sound intensity. This results in a stream of asynchronous events that encode the temporal and spatial information of the scene, with high temporal resolution, low latency, and high dynamic range.
- "In simpler terms, these special sensors work like our eyes and ears. They're designed based on the way our eyes' retinas and ears' cochleae function. But what sets them apart is their unique approach called event-based sensing. Unlike regular sensors that take pictures or recordings at a set speed, these event-based sensors only provide information when there's a change. Whether it's a shift in light or a change in sound, they only capture those moments. Instead of a constant flow of data, you get quick updates that show when and where things change. This gives you highly detailed and fast information about what's happening, with minimal delay and a wide range of details. It's like having sensors that focus on the important stuff, making them efficient and responsive."
The DVS is an imaging sensor that responds to local changes in brightness, and outputs events that indicate the pixel address, the polarity (increase or decrease) of the brightness change, and the timestamp. The DVS can achieve a temporal resolution of microseconds
, a dynamic range of 120 dB
, and a low power consumption of 30 mW
. The DVS can also avoid motion blur and under/overexposure that plague conventional cameras. The DVS can be used for applications such as optical flow estimation, object tracking, gesture recognition, and robotics.
The DAS is an auditory sensor that mimics the cochlea, the auditory inner ear. The DAS takes stereo audio inputs and outputs events that represent the activity in different frequency ranges. The DAS can capture sound signals with a frequency range of 20 Hz to 20 kHz
, a dynamic range of 60 dB, and a temporal resolution of microseconds. The DAS can also extract auditory features such as interaural time difference, harmonicity, and speaker identification.
Both the DVS and the DAS are compatible with neuromorphic computing architectures, such as spiking neural networks, that can process the event data in a parallel and distributed manner. This enables low-power and real-time computation of complex tasks such as scene understanding, speech recognition, and sound localization.
Some examples of recent products that use the DVS and the DAS are:
- The Prophesee Metavision Camera, which is a high-resolution DVS camera that can capture fast and complex motions with minimal data and power consumption.
- The Samsung ISOCELL Slim 3P9, which is a smartphone camera sensor that incorporates a DVS mode to enable fast autofocus and video stabilization.
- The iniVation Dynamic Audio Sensor USB board, which is a binaural DAS board that can be interfaced with standard PCs for sound analysis and processing.
- The Brainchip Akida Neuromorphic System-on-Chip, which is a low-power and scalable chip that can integrate multiple DVS and DAS sensors and perform event-based learning and inference.
I hope this post has given you some insights into the exciting field of neuromorphic sensing, and how it can revolutionize the way we perceive and interact with the world. Thank you for reading!
P.S.: This post is based on one of the tutorials on Neuromorphic Computing at the VLSI Design Conference that happened last week.
Dynamic Vision Sensor (DVS) and the Dynamic Audio Sensor (DAS)
Have you ever wondered how the human eye👁️ and ear👂 can process complex and dynamic scenes with such high speed and accuracy? Imagine if we could design artificial sensors that mimic the biological mechanisms of vision and hearing, and produce data that is more efficient and meaningful than conven
Dynamic Vision Sensor (DVS) and the Dynamic Audio Sensor (DAS)
Kailash Prasad
Kailash Prasad
Design Engineer @ Arm | PMRF | IIRF | nanoDC Lab…
Published Jan 16, 2024+ Follow
Have you ever wondered how the human eye
and ear can process complex and dynamic scenes with such high speed and accuracy? Imagine if we could design artificial sensors that mimic the biological mechanisms of vision and hearing, and produce data that is more efficient and meaningful than conventional sensors.In this post, I will introduce you to two types of neuromorphic sensors: the Dynamic Vision Sensor (DVS) and the Dynamic Audio Sensor (DAS).
These sensors are inspired by the structure and function of the retina and the cochlea, respectively, and use a novel paradigm of event-based sensing. Unlike conventional sensors that capture frames or samples at a fixed rate, event-based sensors only output data when there is a change in the input signal, such as brightness or sound intensity. This results in a stream of asynchronous events that encode the temporal and spatial information of the scene, with high temporal resolution, low latency, and high dynamic range.
- "In simpler terms, these special sensors work like our eyes and ears. They're designed based on the way our eyes' retinas and ears' cochleae function. But what sets them apart is their unique approach called event-based sensing. Unlike regular sensors that take pictures or recordings at a set speed, these event-based sensors only provide information when there's a change. Whether it's a shift in light or a change in sound, they only capture those moments. Instead of a constant flow of data, you get quick updates that show when and where things change. This gives you highly detailed and fast information about what's happening, with minimal delay and a wide range of details. It's like having sensors that focus on the important stuff, making them efficient and responsive."The DVS is an imaging sensor that responds to local changes in brightness, and outputs events that indicate the pixel address, the polarity (increase or decrease) of the brightness change, and the timestamp. The DVS can achieve a temporal resolution of microseconds
, a dynamic range of 120 dB, and a low power consumption of 30 mW. The DVS can also avoid motion blur and under/overexposure that plague conventional cameras. The DVS can be used for applications such as optical flow estimation, object tracking, gesture recognition, and robotics.The DAS is an auditory sensor that mimics the cochlea, the auditory inner ear. The DAS takes stereo audio inputs and outputs events that represent the activity in different frequency ranges. The DAS can capture sound signals with a frequency range of 20 Hz to 20 kHz
, a dynamic range of 60 dB, and a temporal resolution of microseconds. The DAS can also extract auditory features such as interaural time difference, harmonicity, and speaker identification.Both the DVS and the DAS are compatible with neuromorphic computing architectures, such as spiking neural networks, that can process the event data in a parallel and distributed manner. This enables low-power and real-time computation of complex tasks such as scene understanding, speech recognition, and sound localization.
Some examples of recent products that use the DVS and the DAS are:
- The Prophesee Metavision Camera, which is a high-resolution DVS camera that can capture fast and complex motions with minimal data and power consumption.
- The Samsung ISOCELL Slim 3P9, which is a smartphone camera sensor that incorporates a DVS mode to enable fast autofocus and video stabilization.
- The iniVation Dynamic Audio Sensor USB board, which is a binaural DAS board that can be interfaced with standard PCs for sound analysis and processing.
- The Brainchip Akida Neuromorphic System-on-Chip, which is a low-power and scalable chip that can integrate multiple DVS and DAS sensors and perform event-based learning and inference.
I hope this post has given you some insights into the exciting field of neuromorphic sensing, and how it can revolutionize the way we perceive and interact with the world. Thank you for reading!
P.S.: This post is based on one of the tutorials on Neuromorphic Computing at the VLSI Design Conference that happened last week.
Similar threads
