The explosive computation and memory requirements of convolutional neural networks (CNNs) hinder their deployment in resource-constrained devices. Because conventional CNNs perform identical parallelized computations even on redundant pixels, the saliency of various features in an image should be reflected for higher energy efficiency and market penetration. This paper proposes a novel channel and spatial gating network (CSGN) for adaptively selecting vital channels and generating spatial-wise execution masks. A CSGN can be characterized as a dynamic channel and a spatial-aware gating module by maximally utilizing opportunistic sparsity. Extensive experiments were conducted on the CIFAR-10 and ImageNet datasets based on ResNet. The results revealed that, with the proposed architecture, the amount of multiply-accumulate (MAC) operations was reduced by 1.97–11.78× and 1.37–13.12× on CIFAR-10 and ImageNet, respectively, with negligible accuracy degradation in the inference stage compared with the baseline architectures.

The advancement of multi-level cell technology that enables storing multiple bits in a single NAND flash memory cell has increased the density and affordability of solid-state drives (SSDs). However, increased latency asymmetry between read and write (R/W) intensifies the severity of R/W interference, so reads cannot be processed for a long time owing to the extended flash memory resource occupancy of writing. Existing flash translation layer (FTL)-level mitigation techniques can allocate flash memory resources in a balanced manner taking R/W interference into account; however, due to the inefficient utilization of parallel flash memory resources, the effect on performance enhancement is restrictive. From the perspectives of the predicted access pattern and available concurrency of flash memory resources, we propose a parallelism-aware channel partition (PACP) scheme that prevents SSD performance degradation caused by R/W interference. Moreover, an additional performance improvement is achieved by reallocating interference-vulnerable page using leveraged garbage collection (GC) migration. The evaluation results showed that compared with the existing solution, PACP reduced the average read latency by 11.6% and average write latency by 6.0%, with a negligible storage overhead.