This article introduces the firstgeneral reuse-centric acceleratorfor CNN inferences. Unlike prior work that exploits similarities only across consecutive video frames,general reuse-centric acceleratoris able to discover similarities among arbitrary patches within an image or across independent images, and translate them into computation time and energy savings. Experiments show that the accelerator complements both prior software-based CNN and various CNN hardware accelerators, producing up to 14.96X speedups for similarity discovery, up to 2.70X speedups for overall inference.

Reuse-centric convolutional neural networks (CNN) acceleration speeds up CNN inference by reusing computations for similar neuron vectors in CNN’s input layer or activation maps. This new paradigm of optimizations is, however, largely limited by the overheads in neuron vector similarity detection, an important step in reuse-centric CNN. This article presents an in-depth exploration of architectural support for reuse-centric CNN. It addresses some major limitations of the state-of-the-art design and proposes a novel hardware accelerator that improves neuron vector similarity detection and reduces the energy consumption of reuse-centric CNN inference. The accelerator is implemented to support a wide variety of neural network settings with a banked memory subsystem. Design exploration is performed through RTL simulation and synthesis on an FPGA platform. When integrated into Eyeriss, the accelerator can potentially provide improvements up to 7.75 \( \times \) in performance. Furthermore, it can reduce the energy used for similarity detection up to 95.46%, and it can accelerate the convolutional layer up to 3.63 \( \times \) compared to the software-based implementation running on the CPU.