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.

In the recent past, real-time video processing using state-of-the-art deep neural networks (DNN) has achieved human-like accuracy but at the cost of high energy consumption, making them infeasible for edge device deployment. The energy consumed by running DNNs on hardware accelerators is dominated by the number of memory read/writes and multiplyaccumulate (MAC) operations required. As a potential solution, this work explores the role of activation sparsity in efficient DNN inference. As the predominant operation in DNNs is matrix-vector multiplication of weights with activations, skipping operations and memory
fetches where (at least) one of them is zero can make inference more energy efficient. Although spatial sparsification of activations is researched extensively, introducing and exploiting temporal sparsity is much less explored in DNN literature. This work presents a new DNN layer (called temporal delta layer) whose primary objective is to induce temporal activation sparsity during training. The temporal delta layer promotes activation sparsity by performing delta operation facilitated by activation quantization and l1 norm based penalty to the cost function. During inference, the resulting model acts as a conventional quantized
DNN with high temporal activation sparsity. The new layer was incorporated as a part of the standard ResNet50 architecture to be trained and tested on the popular human action recognition dataset (UCF101). The method caused 2x improvement in activation sparsity, with 5% accuracy loss.