Multi-Function Multi-Way Analog Technology for Sustainable Machine Intelligence Computation
CoRR(2024)
摘要
Numerical computation is essential to many areas of artificial intelligence
(AI), whose computing demands continue to grow dramatically, yet their
continued scaling is jeopardized by the slowdown in Moore's law. Multi-function
multi-way analog (MFMWA) technology, a computing architecture comprising arrays
of memristors supporting in-memory computation of matrix operations, can offer
tremendous improvements in computation and energy, but at the expense of
inherent unpredictability and noise. We devise novel randomized algorithms
tailored to MFMWA architectures that mitigate the detrimental impact of
imperfect analog computations while realizing their potential benefits across
various areas of AI, such as applications in computer vision. Through analysis,
measurements from analog devices, and simulations of larger systems, we
demonstrate orders of magnitude reduction in both computation and energy with
accuracy similar to digital computers.
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