Brain-inspired feature extraction for near sensor extreme edge processing with Spiking Neural Networks

Abstract

Motivated by the desire to bring intelligent processing at the Edge, enabling online learning on resource- and latency-constrained embedded devices has become increasingly appealing, as it has the potential to tackle a wide range of challenges: on the one hand, it can deal with on-the-fly adaptation to fast sensor-generated streams of data under changing environments and on the other hand, it can address a variety of challenges associated with offline training in the cloud, such as incurred energy consumption of sensor data transfers and extra memory storage for the training samples, but also data privacy and security concerns. Concurrently, maintaining low-latency and power-efficient inference is paramount for edge AI computing systems, and thus learning/adapting online with minimal incurred overhead is crucial.

In this work, we propose EON-1, an Edge ONline Learning SCNN (Spiking Convolutional Neural Network) processor with 1-bit synaptic weights, 1-spike per neuron and 1-neuron updated per input, which we have benchmarked for both ASIC and FPGA platforms. Our key contribution is proposing a binary and stochastic SDTP rule which, benchmarked in an ASIC node, achieves less than 1% energy overhead for inference. To our knowledge, our solution incurs the least energy overhead for inference, compared to state-of-the-art solutions, showing a better efficiency by at least a factor of 10x. We also report 94% and 77.65% accuracy on the MNIST and Fashion-MNIST classification tasks, and we achieve 0.09pJ/SOP and 1.5pJ/SOP energy efficiency during inference and learning, respectively. We extend our solution to demonstrate a practical use-case of performing inference in real-time UHD videos while coping with streaming data and we showcase 60 FPS UHD video processing.