Just came across this paper published 16.5.24.
The authors are known to me from other works co- authored with Adam Osseiran Chair of the Brainchip Scientific Advisory Board. They are all based at Edith Cowan University in Western Australia.
The following extracted paragraphs appear in the paper. The entire paper gives an insight into what work would be required to bring the AKIDA Prophesee project to a successful commercial conclusion.
“4.1.6. Akida
Akida, created by Australian company BrainChip, stands out as the first commercially available neuromorphic processor released in August 2021 [165], with NASA and other companies participating in the early access program. Positioned as a power-efficient event-based processor for edge computing, Akida functions independently of an external CPU and consumes 100 μW to 300 mW for diverse tasks. Boasting a processing capability of 1,000 frames/Watt, Akida currently supports convolutional and fully connected networks, with potential future backing for various neural network types. The chip facilitates the conversion of ANN networks into SNN for execution.
A solitary Akida chip within a mesh network incorporates 80 Neural Processing Units, simulating 1,200,000 neurons and 10,000,000,000 synapses. Fabricated using TSMC technology, a second-generation 16 nm chip was unveiled in 2022. The Akida ecosystem encompasses a free chip emulator, the MetaTF framework for network transformation, and pre-trained models. Designing for Akida necessitates consideration of layer parameter limitations.
A notable feature of Akida is its on-chip support for incremental, one-shot, and continuous learning. BrainChip showcased applications at the AI Hardware Summit 2021, highlighting human identification after a single encounter and a smart speaker using local training for voice recognition. The proprietary homeostatic STDP algorithm supports learning, with synaptic plasticity limited to the last fully connected layer. Another demonstrated application involved the classification of fast- moving objects using an event-based approach, effectively detecting objects even when positioned off-centre and appearing blurred.”
“ 5.3. Neuromorphic SLAM Challenges
Developing SLAM algorithms that effectively utilize event-based data from event cameras and harness the computational capabilities of neuromorphic processors presents a significant challenge. These algorithms must be either heavily modified or newly conceived to fully exploit the strengths of both technologies. Furthermore, integrating data from event cameras with neuromorphic processors and other sensor modalities, such as IMUs or traditional cameras, necessitates the development of new fusion techniques. Managing the diverse data formats, temporal characteristics, and noise profiles from these sensors while maintaining consistency and accuracy throughout the SLAM process will be a complex task.
In terms of scalability, expanding event cameras and neuromorphic processor-based SLAM systems to accommodate large-scale environments with intricate dynamics will pose challenges in computational resource allocation. It is essential to ensure scalability while preserving real-time performance for practical deployment. Additionally, event cameras and neuromorphic processors must adapt to dynamic environments where scene changes occur rapidly. Developing algorithms capable of swiftly updating SLAM estimates based on incoming event data while maintaining robustness and accuracy is critical.
Leveraging the learning capabilities of neuromorphic processors for SLAM tasks, such as map building and localization, necessitates the design of training algorithms and methodologies proficient in learning from event data streams. The development of adaptive learning algorithms capable of enhancing SLAM performance over time in real-world environments presents a significant challenge. Moreover, ensuring the correctness and reliability of event camera and neuromorphic processor- based SLAM systems poses hurdles in verification and validation. Rigorous testing methodologies
must also be developed to validate the performance and robustness of these systems. If these challenges can be overcome, the potential rewards are significant, however.”
My opinion only DYOR
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