DNN Engine: A 28-nm Timing-Error Tolerant Sparse Deep Neural Network Processor for IoT Applications

Abstract

This paper presents a 28-nm system-on-chip (SoC) for Internet of things (IoT) applications with a programmable accelerator design that implements a powerful fully connected deep neural network (DNN) classifier. To reach the required low energy consumption, we exploit the key properties of neural network algorithms: parallelism, data reuse, small/sparse data, and noise tolerance. We map the algorithm to a very large scale integration (VLSI) architecture based around an single-instruction, multiple-data data path with hardware support to exploit data sparsity by completely eliding unnecessary computation and data movement. This approach exploits sparsity, without compromising the parallel computation. We also exploit the inherent algorithmic noise-tolerance of neural networks, by introducing circuit-level timing violation detection to allow worst case voltage guard-bands to be minimized. The resulting intermittent timing violations may result in logic errors, which conventionally need to be corrected. However, in lieu of explicit error correction, we cope with this by accentuating the noise tolerance of neural networks. The measured test chip achieves high classification accuracy (98.36% for the MNIST test set), while tolerating aggregate timing violation rates > 10-1. The accelerator achieves a minimum energy of 0.36 μ J /inference at 667 MHz; maximum throughput at 1.2 GHz and 0.57 μ J /inference; or a 10% margined operating point at 1 GHz and 0.58 μ J /inference.