E3: A HW/SW Co-design Neuroevolution Platform for Autonomous Learning in Edge Device

The true potential of AI can be realized once we move beyond supervised training using labelled datasets on the cloud to autonomous learning on edge devices. While techniques like Reinforcement Learning are promising for their autonomous learning ability, they exhibit high compute and memory requirements due to gradient computations, making them prohibitive for edge deployment. In this paper, we propose E3, a HW/SW co-designed edge learning system on a FPGA. E3 uses a gradient-free approach called neuro-evolution (NE) to evolve the neural network (NN) topology and weights dynamically. The NNs evolved using NE are highly irregular, and a population of such NNs need to be evaluated quickly in order for the NE algorithm to make progress. To address this, we develop INAX, a specialized accelerator inside E3 for efficient irregular network computation. INAX leverages multiple avenues of parallelism both within and across the evolved NNs. E3 shows averaged 30× speedup than CPU-based solution across a suite of OpenAI environments.