General-Purpose Code Acceleration with Limited-Precision Analog Computation

As improvements in per-transistor speed and energy effi-ciency diminish, radical departures from conventional ap-proaches are becoming critical to improving the performance and energy efficiency of general-purpose processors. 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 out-line the challenges of taking an analog approach, including restricted-range value encoding, limited precision in computa-tion, circuit inaccuracies, noise, and constraints on supported topologies. We address these limitations with a combination of circuit techniques, a hardware/software interface, neural-network training techniques, and compiler support. Analog neural acceleration provides whole application speedup of 3.7 × and energy savings of 6.3 × 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 of approximation-tolerant, emerging applica-tions including financial analysis, signal processing, robotics, 3D gaming, compression, and image processing. 1