  Bias-scalable analog computing is attractive for implementing machine
learning (ML) processors with distinct power-performance specifications. For
instance, ML implementations for server workloads are focused on higher
computational throughput for faster training, whereas ML implementations for
edge devices are focused on energy-efficient inference. In this paper, we
demonstrate the implementation of bias-scalable approximate analog computing
circuits using the generalization of the margin-propagation principle called
shape-based analog computing (S-AC). The resulting S-AC core integrates several
near-memory compute elements, which include: (a) non-linear activation
functions; (b) inner-product compute circuits; and (c) a mixed-signal
compressive memory, all of which can be scaled for performance or power while
preserving its functionality. Using measured results from prototypes fabricated
in a 180nm CMOS process, we demonstrate that the performance of computing
modules remains robust to transistor biasing and variations in temperature. In
this paper, we also demonstrate the effect of bias-scalability and
computational accuracy on a simple ML regression task.
