SIMCNN – Exploiting Computational Similarity to Accelerate CNN Training in Hardware
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Convolution neural networks (CNN) are computation intensive to train. It consists of a substantial number of multidimensional dot products between many kernels and inputs. We observe that there are notable similarities among the vectors extracted from inputs (i.e., input vectors). If one input vector is similar to another one, its computations with the kernels are also similar to those of the other and therefore, can be skipped by reusing the already-computed results. Based on this insight, we propose a novel scheme based on locality sensitive hashing (LSH) to exploit the similarity of computations during CNN training in a hardware accelerator. The proposed scheme, called SIMCNN, uses a cache (SIMCACHE) to store LSH signatures of recent input vectors along with the computed results. If the LSH signature of a new input vector matches with that of an already existing vector in the SIMCACHE, the already-computed result is reused for the new vector. SIMCNN is the first work that exploits computational similarity for accelerating CNN training in hardware. The paper presents a detailed design, workflow, and implementation of SIMCNN. Our experimental evaluation with four different deep learning models shows that SIMCNN saves a significant number of computations and therefore, improves training time up to 43
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