UC San Diego

Machine learning and related statistical signal processing are expected to endow sensor networks with adaptive machine intelligence and greatly facilitate the Internet of Things (IoT). As such, architectures embedding adaptive and learning algorithms on-chip are oft-ignored by system architects and design engineers, and present a new set of design trade-offs. We focus on topologies efficiently implementing mixed-signal matrix-vector multiplication for applications in spatial filtering for IoT, where substantial processing gain in the analog domain alleviates the need for highly accurate and energy-consuming analog-to-digital conversion. We present a micropower, high-dynamic-range multichannel multiple-input multiple-out (MIMO) mixed-signal linear transform system, with analog signal path and digital coefficient control, composed of an array of 14-bit Nested Thermometer Multiplying DACs (NTMDACs) implementing analog multiplication, and variable gain amplifier (VGA) implementing accumulation. Implemented in 65nm CMOS, the NTMDAC MISO system-on-chip measures 84 dB in interference suppression at 2 pJ of energy per mixed-signal multiply-accumulate. We demonstrate state-of-the art performance on two tasks, spectrally oblivious interference suppression in communication signals and EEG signal separation. We then provide experimental demonstration of the use of a MIMO mixed-signal linear-transform system within a radio-frequency receiver chain. Over-the-air experiments demonstrating signal separation for two broad-band modulated signals further validate the adaptive beamforming capabilities under severe multipath conditions even in the absence of line-of-sight communication path.

In order to mitigate adverse effects of radix errors and capacitive mismatch encountered in compact low-power realizations of high-resolution, high-dimensional MIMO analog processing systems, we introduce Stochastic Successive Approximation, or S2A, as an on-line adaptive optimization algorithm amenable to efficient implementation in massively parallel analog hardware. S2A offers a direct alternative to stochastic gradient descent overcoming several of its shortcomings, such as its sensitivity to analog mismatch model errors, while improving on the rate of convergence for high-dimensional analog computation. The S2A algorithm enables convergence to values closer to the optimal when facing non-convex optimization landscapes induced by mismatch in capacitive multiplying digital-to-analog converter components when applied to adaptive analog signal processing. We experimentally demonstrate, in fewer than 25 iteratations of S2A, 65 dB of processing gain in adaptive beamforming, over-the-air, multipath interferer suppression.