  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.
