The recent DeepBench proposal released by Baidu includes several core DSP routines commonly used for the training and inferencing of Deep Neural Networks (DNN). On the surface, this is a well-intentioned and useful benchmark proposal. However, depending on the way benchmarks like these are defined, they can either encourage or discourage innovation across the industry. The lofty and admirable goal set by the Baidu team is to encourage a 100x improvement in performance for deep learning, and the proposed benchmark provides a way to measure the increase in performance by given hardware systems.
The demand for Deep Learning (DL) systems has grown exponentially over the last four years. Market research firm Tractica projects the DL systems market will grow to over $10.4 Billion by 2025, and IBM’s CEO believes that DL and broader Artificial Intelligence (AI) will have a $2 Trillion impact on businesses by 2025. However you slice and dice the market, the demand for better, faster DL systems is exploding.