Good to see the Open Neuromorphic community have us included, as they should.
These are also the sorts of people we need testing and pushing our Akidas limits and capabilities.
About the community below and our info after that with some related publications, which I believe we've already sourced independently previously from memory.
Could be a resource checking in on every so often for any snippets.
Learn about BrainChip's neuromorphic hardware: Akida
open-neuromorphic.org
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About
Discover Open Neuromorphic (ONM), a collaborative community for neuromorphic computing enthusiasts. Explore tools, frameworks, and educational content to fuel your passion.
Open Neuromorphic is created by a loose collective of
open source collaborators across academia, industry and individual contributors. Most of us have never met in person before but have started contributing to a common or each other’s projects!
What connects us is the love for building tools that can be used in the neuromorphic community and we want to share ownership of this vision. If you feel like that resonates with you, please don’t hesitate to get in touch!
Open Neuromorphic (ONM) provides the following things:
- A curated list of software frameworks to make it easier to find the tool you need.
- A platform for your code. If you wish to create a new repository or migrate your existing code to ONM, please get in touch with us.
- Educational content to get you started in the neuromorphic world.
- Events about neuromorphic research and software, with contributions from both academia and industry.
Projects that we list here can fall into this non-exclusive list of categories:
- Spiking Neural Networks (SNNs) training and/or inference, for both ML and neuroscience application.
- Event-based sensors data handling.
- Digital hardware designs for neuromorphic applications.
- Mixed-signal hardware designs for neuromorphic applications.
Get in touch with us if you wish to give a talk, write an article or to know more about the neuromorphic world.
Akida - BrainChip
Learn about BrainChip's neuromorphic hardware: Akida
Akida At A Glance
Announced Date: 2023-01-29
Release Date: 2023-01-29
Status: Released
Chip Type: Digital
Neurons: Configurable
Synapses: 8-Mb SRAM
On-Chip Learning: true
Power: ~30 mW
Software: MetaTF
Applications: Smart sensing, one-shot learning
BrainChip's Akida is an ultra-low-power neuromorphic processor inspired by the brain's neural architecture. It accelerates complex AI at the edge through event-based processing, on-chip learning abilities, and support for advanced neural networks like CNNs, RNNs & custom Temporal Event-based Nets.
Developed By:
BrainChip
company/brainchip-holdings-limited/ @BrainChip_inc BrainChip
Official Product Page
Overview
Inspired by the human brain’s neural architecture, Akida aims to deliver high-performance artificial intelligence capabilities at the edge while being extremely energy efficient.
The Akida processor is designed to accelerate neural networks including convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs) and Vision Transformers (ViTs) directly in hardware. A key feature is its support for a novel neural network architecture called Temporal Event-based Neural Nets (TENNs) which are optimized for processing complex time-series data efficiently.
Akida employs an event-based processing approach where computations are only performed when new sensory input is received, dramatically reducing the number of operations. This also enables event-based communication between processor nodes without CPU intervention. The architecture further supports on-chip learning, allowing models to adapt without having to connect to the cloud.
The second generation Akida platform adds capabilities such as support for 8-bit weights/activations, improved vision transformer acceleration, multi-pass sequential processing, and configurable local scratchpads to optimize memory access. It is designed to run larger neural networks across multiple chips while minimizing latency.
Akida leverages standard machine learning frameworks like TensorFlow and development platforms like Edge Impulse for model training and deployment. BrainChip also provides complementary software tools like MetaTF to optimize models for the Akida hardware. Pre-built Akida-compatible models are also offered through a model zoo.
Akida targets applications spanning industrial automation, automotive, healthcare, consumer electronics and more. Use cases include predictive maintenance, in-cabin monitoring, vital sign prediction, home automation, surveillance and more. Its efficiency and on-device learning abilities aim to enable a new class of continuously adaptive, secure and private AI implementations at the edge.
Related publications