MOCHA: Morphable Locality and Compression Aware Architecture for Convolutional Neural Networks

Abstract

Today, machine learning based on neural networks has become mainstream, in many application domains. A small subset of machine learning algorithms, called Convolutional Neural Networks (CNN), are considered as state-ofthe- art for many applications (e.g. video/audio classification). The main challenge in implementing the CNNs, in embedded systems, is their large computation, memory, and bandwidth requirements. To meet these demands, dedicated hardware accelerators have been proposed. Since memory is the major cost in CNNs, recent accelerators focus on reducing the memory accesses. In particular, they exploit data locality using either tiling, layer merging or intra/inter feature map parallelism to reduce the memory footprint. However, they lack the flexibility to interleave or cascade these optimizations. Moreover, most of the existing accelerators do not exploit compression that can simultaneously reduce memory requirements, increase the throughput, and enhance the energy efficiency. To tackle these limitations, we present a flexible accelerator called MOCHA. MOCHA has three features that differentiate it from the state-of-the-art: (i) the ability to compress input/ kernels, (ii) the flexibility to interleave various optimizations, and (iii) intelligence to automatically interleave and cascade the optimizations, depending on the dimension of a specific CNN layer and available resources. Post layout Synthesis results reveal that MOCHA provides up to 63% higher energy efficiency, up to 42% higher throughput, and up to 30% less storage, compared to the next best accelerator, at the cost of 26-35% additional area.