Stochastic Mixed-Signal VLSI Architecture for High-Dimensional Kernel Machines

Part of Advances in Neural Information Processing Systems 14 (NIPS 2001)

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Authors

Roman Genov, Gert Cauwenberghs

Abstract

A mixed-signal paradigm is presented for high-resolution parallel inner- product computation in very high dimensions, suitable for ef´Čücient im- plementation of kernels in image processing. At the core of the externally digital architecture is a high-density, low-power analog array performing binary-binary partial matrix-vector multiplication. Full digital resolution is maintained even with low-resolution analog-to-digital conversion, ow- ing to random statistics in the analog summation of binary products. A random modulation scheme produces near-Bernoulli statistics even for highly correlated inputs. The approach is validated with real image data, and with experimental results from a CID/DRAM analog array prototype in 0.5