Toward General-Purpose Code Acceleration with Analog Computation

We propose a solution—from circuit to compiler—that enables general-purpose use of limited-precision, analog hardware to accelerate “approximable” code—code that can tolerate imprecise execution. We utilize an algorithmic transformation that automatically converts approximable regions of code from a von Neumann model to an “analog” neural model. We outline the challenges of taking an analog approach, including restricted-range value encoding, limited precision in computation, circuit inaccuracies, noise, and constraints on supported topologies. We address these limitations with a combination of circuit techniques, a novel hardware/software interface, neural-network training techniques, and compiler support. Analog neural acceleration provides whole application speedup of 3.3× and and energy savings of 12.1× with quality loss less than 10% for all except one benchmark. These results show that using limited-precision analog circuits for code acceleration, through a neural approach, is both feasible and beneficial over a range emerging applications.