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NNest: Early-Stage Design Space Exploration Tool for Neural Network Inference Accelerators

Deep neural network (DNN) has achieved spectacular success in recent years. In response to DNN's enormous computation demand and memory footprint, numerous inference accelerators have been proposed. However, the diverse nature of DNNs, both at the algorithm level and the parallelization level, makes it hard to arrive at an "one-size-fits-all" hardware design. In this paper, we develop NNest, an early-stage design space exploration tool that can speedily and accurately estimate the area/performance/energy of DNN inference accelerators based on high-level network topology and architecture traits, without the need for low-level RTL codes. Equipped with a generalized spatial architecture framework, NNest is able to perform fast high-dimensional design space exploration across a wide spectrum of architectural/micro-architectural parameters. Our proposed novel date movement strategies and multi-layer fitting schemes allow NNest to more effectively exploit parallelism inherent in DNN. Results generated by NNest demonstrate: 1) previously-undiscovered accelerator design points that can outperform state-of-the-art implementation by 39.3% in energy efficiency; 2) Pareto frontier curves that comprehensively and quantitatively reveal the multi-objective tradeoffs in custom DNN accelerators; 3) holistic design exploration of different level of quantization techniques including recently-proposed binary neural network (BNN).