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Abstract:
A mixed-signal paradigm is presented for high-resolution
parallel inner-product computation in very high dimensions,
suitable for efficient implementation 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,
owing 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 μm CMOS.
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