Abstract
In this paper, we analyze the suitability of convolutional neural network (CNN) inference workloads on a phase-change memory (PCM) platform. CNN inference has an average of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$14\times$</tex> more read requests than write requests (i.e., read dominant) and a significantly low last-level cache misses per kilo instructions (LLC MPKI) of 2 on average (i.e., computation intensive). In addition, to compare the latency and energy of PCM and DRAM systems, we evaluate CNN inference workloads on two memory systems through a memory simulator. As a result, compared to DRAM, PCM can save total energy by 54% on average, but instruction per cycle (IPC) of PCM is reduced by an average of 28%. In conclusion, CNN inference is a workload suitable for PCM in terms of energy efficiency, but it must be accompanied by a scheme to improve IPC for practical use.
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