Kavli Affiliate: Xiang Zhang
| First 5 Authors: Jian Zhang, Xiang Zhang, Jingchen Huang, Jilin Zhang, Hong Chen
| Summary:
Developing asynchronous neuromorphic hardware to meet the demands of diverse
real-life edge scenarios remains significant challenges. These challenges
include constraints on hardware resources and power budgets while satisfying
the requirements for real-time responsiveness, reliable inference accuracy, and
so on. Besides, the existing system-level simulators for asynchronous
neuromorphic hardware suffer from runtime limitations. To address these
challenges, we propose an Asynchronous Neuromorphic algorithm/hardware
Co-Exploration Framework (ANCoEF) including multi-objective reinforcement
learning (RL)-based hardware architecture optimization method, and a fully
asynchronous simulator (TrueAsync) which achieves over 2 times runtime speedups
than the state-of-the-art (SOTA) simulator. Our experimental results show that,
the RL-based hardware architecture optimization approach of ANCoEF outperforms
the SOTA method by reducing 1.81 times hardware energy-delay product (EDP) with
2.73 times less search time on N-MNIST dataset, and the co-exploration
framework of ANCoEF improves SNN accuracy by 9.72% and reduces hardware EDP by
28.85 times compared to the SOTA work on DVS128Gesture dataset. Furthermore,
ANCoEF framework is evaluated on external neuromorphic dataset CIFAR10-DVS, and
static datasets including CIFAR10, CIFAR100, SVHN, and Tiny-ImageNet. For
instance, after 26.23 ThreadHour of co-exploration process, the result on
CIFAR10-DVS dataset achieves an SNN accuracy of 98.48% while consuming hardware
EDP of 0.54 s nJ per sample.
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