混合和混合：MACAM启用的模拟激活用于节能神经加速
Fuse and Mix: MACAM-Enabled Analog Activation for Energy-Efficient Neural Acceleration
摘要：
模拟计算已被认为是一个有前途的低功耗替代数字对应的神经网络加速。然而，传统的模拟计算主要是以混合信号的方式。繁琐的模拟/数字（A/D）转换成本极大地限制了整个系统的能效。在这项工作中，我们设计了一个有效的模拟激活单元与磁性隧道结（MTJ）为基础的模拟内容可寻址存储器（MACAM），同时实现非线性激活和A/D转换融合的方式。为了弥补新生的，因此目前有限的MACAM表示能力，我们建议将我们的模拟激活单元与数字激活混合。一个完全差分框架，SuperMixer，开发搜索优化的激活工作负载分配，适应各种激活能量约束。我们所提出的方法的有效性进行了评估硅光子加速器。与标准激活实现相比，我们的混合激活系统与搜索分配可以达到竞争力的准确性与$>$ 60%的A/D转换和激活节能。
英文摘要：
Analog computing has been recognized as a promising low-power alternative to digital counterparts for neural network acceleration. However, conventional analog computing is mainly in a mixed-signal manner. Tedious analog/digital (A/D) conversion cost significantly limits the overall system's energy efficiency. In this work, we devise an efficient analog activation unit with magnetic tunnel junction (MTJ)-based analog content-addressable memory (MACAM), simultaneously realizing nonlinear activation and A/D conversion in a fused fashion. To compensate for the nascent and therefore currently limited representation capability of MACAM, we propose to mix our analog activation unit with digital activation dataflow. A fully differential framework, SuperMixer, is developed to search for an optimized activation workload assignment, adaptive to various activation energy constraints. The effectiveness of our proposed methods is evaluated on a silicon photonic accelerator. Compared to standard activation implementation, our mixed activation system with the searched assignment can achieve competitive accuracy with $>$60% energy saving on A/D conversion and activation.
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