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Hot-Spot Suppression for Resource-Constrained Image Recognition Devices With Nonvolatile Memory

Resource-constrained devices with convolutional neural networks (CNNs) for image recognition are becoming popular in various Internet of Things and surveillance applications. They usually have a low-power CPU and limited CPU cache space. In such circumstances, nonvolatile memory (NVM) has great potential to replace DRAM as main memory to improve overall energy efficiency and provide larger main-memory space. However, due to the iterative access pattern, performing CNN-based image recognition may introduce some write hot-spots on the NVM main memory. These write hot-spots may lead to reliability issues due to limited write endurance of NVM. In order to improve the endurance of NVM main memory, this paper leverages the CPU cache pinning technique and exploits the iterative access pattern of CNN to resolve the write hot-spot effect. In particular, we present a CNN-aware self-bouncing pinning strategy to minimize the maximal write cycles in NVM cells by proactively fastening CPU cache lines, so as to effectively suppress the write hot-spots to NVM main memory with limited performance degradation. The proposed strategy was evaluated by a series of intensive experiments and the results are encouraging.