Great watch ... Thanks MrNick
Hey Guys,
Did you bother to check out the cars number plate ?
SEQ (Sequence) 1010 (binary) E (Edge)
Brainchips in there somewhere
Great watch ... Thanks MrNick
Aha! Penny's dropped. Remember when 8-bit weights were announced?
This change may be to accommodate 8-bit weights/activations.
The ALUs may be more efficient at handling the multi-bit "spikes" than the original Akida configuration.
I found this Sanskrit engraving on Eric von Dunnycan's tomb:
https://doc.brainchipinc.com/_modules/akida_models/imagenet/model_mobilenet.html
...
weight_quantization (int, optional): sets all weights in the model to have a particular quantization bitwidth except for the weights in the first layer.
Defaults to 0.
* '0' implements floating point 32-bit weights.
* '2' through '8' implements n-bit weights where n is from 2-8 bits.
activ_quantization (int, optional): sets all activations in the model to have a particular activation quantization bitwidth.
Defaults to 0.
...
input_scaling (tuple, optional): scale factor and offset to apply to
inputs. Defaults to (128, -1). Note that following Akida convention, the scale factor is an integer used as a divider.
...
© Copyright 2022, BrainChip Holdings Ltd. All Rights Reserved.
If I recall correctly, it is only the weights that are 8-bit, and only for the purpose of compatibility with 3rd party model libraries.
If there are 8-bit weights and 4-bit activations, an 8*4 matrix would be used.
Nice work TECH ...Hey Guys,
Did you bother to check out the cars number plate ?
SEQ (Sequence) 1010 (binary) E (Edge)
Brainchips in there somewhere
I know there has been some, shall we say conjecture recently on Nviso but this popped up in a Google search & dated week or so ago.
Pushing the neuromorphic mobile EVK.
Works for me if can get some teaction
Mobile Phones - NVISO
HUMAN BEHAVIOUR AI MOBILE PHONES NVISO’s Human Behaviour AI SDK allows application developers to build innovative solutions to transform our lives using AI on mobile phones. Understand people and their behavior to make autonomous devices safe, secure, and personalized for humans. Download Trial...nviso.ai
HUMAN BEHAVIOUR AI
MOBILE PHONES
NVISO’s Human Behaviour AI SDK allows application developers to build innovative solutions to transform our lives using AI on mobile phones. Understand people and their behavior to make autonomous devices safe, secure, and personalized for humans.
DOWNLOAD TRIAL EVK
AI-ENABLED
HUMAN MACHINE INTERFACES
NVISO’s Mobile SDK provides a robust real-time human behaviour AI API, NVISO Neuro Models™ interoperable and optimised for neuromorphic computing, the ability for flexible sensor integration and placement while delivering faster development cycles and time-to-value for software developers and integrators. It enables solutions that can sense, comprehend, and act upon human behavior including emotion recognition, gaze detection, distraction detection, drowsiness detection, gesture recognition, 3d face tracking, face analysis, facial recognition, object detection, and human pose estimation. Designed for real-world environments using edge computing it uniquely targets deep learning for embedded systems,
NVISO delivers real-time perception and observation of people and objects in contextual situations combined with the reasoning and semantics of human behavior based on trusted scientific research. The NVISO Mobile SDK is supported through a long term maintenance agreement for multi-party implementation of tools for AI systems development and can be used with large-scale neuromorphic computing systems. When used with neuromorphic chips, the NVISO Mobile SDK can be used to build gaze detection systems, distraction and drowsiness detection systems, facial emotion recognition software, and a range of other applications of neuromorphic computing where understanding human behaviour in real-time is mission critical.
View attachment 39212
Hi DHi Fmf,
This bit is especially interesting:
Mobile Phones - NVISO
HUMAN BEHAVIOUR AI MOBILE PHONES NVISO’s Human Behaviour AI SDK allows application developers to build innovative solutions to transform our lives using AI on mobile phones. Understand people and their behavior to make autonomous devices safe, secure, and personalized for humans. Download Trial...nviso.ai
MICROCONTROLLER UNIT (MCU)
AI functionality is implemented in low-cost MCUs via inference engines specifically targeting MCU embedding design requirements which are configured for low-power operations for continuous monitoring to discover trigger events in a sound, image, or vibration and more. In addition, the availability of AI-dedicated co-processors is allowing MCU suppliers to accelerate the deployment of machine learning functions.
Do we know where nViso gets their MCUs?Hi D
You thinking a potential tie in with someone in particular
Wondered if you were fishing in Renesas' pondDo we know where nViso gets their MCUs?
Akida 1500 could work with MCUs. All that is needed is sufficient processing power and memory to enable configuration od Akida and to handle the output from Akida.
We know Renesas has a licence for 2 nodes (8 NPUs), but I don't know how many nodes are required for nViso's functions like face recognition.
nViso is a BrainChip partner, so it may not need a licence:
View attachment 39219
BrainChip and NVISO are targeting battery-powered applications in robotics and mobility devices addressing the need for high levels of AI performance in ultra low power environments. Implementing NVISO’s AI solutions with BrainChip’s Akida drive next generation solutions.
This is another signpost to Akida:
NVISO NEURO MODELS™
ULTRA-EFFICIENT DEEP LEARNING AT THE EDGE
NVISO Neuro Models™ are purpose built for a new class of ultra-efficient machine learning processors designed for ultra-low power edge devices. Supporting a wide range of heterogenous computing platforms ranging from CPU, GPU, DSP, NPU, and neuromorphic computing they reduce the high barriers-to-entry into the AI space through cost-effective standardized AI Apps which work optimally at the extreme edge (low power, on-device, without requiring an internet connection). NVISO uses low and mixed precision activations and weights data types (1 to 8-bit) combined with state-of-the-art unstructured sparsity to reduce memory bandwidth and power consumption. Proprietary compact network architectures can be fully sequential suitable for ultra-low power mixed signal inference engines and fully interoperable with both GPUs and neuromorphic processors.
But just to muddy the waters, Akida is qualified across ARMs range of processors, not to mention Intel, MegaChips, ...Wondered if you were fishing in Renesas' pond
Not inconceivable....they taped out for an unnamed 3rs party
But just to muddy the waters, Akida is qualified across ARMs range of processors, not to mention Intel, MegaChips, ...