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An Energy-Efficient Reconfigurable Processor forBinary- and Ternary-Weight Neura
提出一种能效优化的可重构处理器，用于加速二值和三值权重神经网络的推理。
28nm CMOS
能效优化可重构处理器二值权重神经网络三值权重神经网络卷积优化
▸创新点1：基于特征积的卷积方法（FIBC）是一种算法创新，通过将卷积操作转换为特征积分运算，显著降低了算术复杂度，提升了计算效率，适用于二进制和三元权重神经网络。
▸创新点2：核变换特征重构卷积方法（KTFR）是一种算法创新，通过核变换和特征重构技术，有效减少了卷积操作中的冗余计算，进一步优化了计算性能。
▸创新点3：分层负载均衡机制（HLBM）是一种系统创新，通过动态分配计算资源并消除零值计算，提高了资源利用率和能效，实测能效提升1.25倍。
▸创新点4：联合优化方法是一种系统创新，通过为每一层卷积搜索最优计算模式，实现了整体性能的最大化，能效提升2.24倍。
Abstract
Due to less memory requirement, low computation
overhead and negligible accuracy degradation, deep neural net-
works with binary/ternary weights (BTNNs) have been widely
employed on low-power mobile and Internet of Things (IoT)
devices with limited storage capacity. Some hardware imple-
mentations have been proposed to accelerate the inference of
BTNNs by utilizing the multiplication-free feature. However,
some implicit characteristics in BTNN convolution, such as
high arithmetic complexity and