In a world driven by technology and hand-held devices, there is ubiquitous demand for high-performance, low-energy processing engines. In this thesis, we present rapidly switched analog circuit (RSAC), a new circuit architecture, to implement an energy-efficient mixed-signal dot product (DP) kernel for machine learning and signal processing applications. RSAC operates by fast switching the analog inputs to the output via variable width digital pulses. A description of the different components of RSAC, along with a detailed accuracy and energy consumption analysis is presented. We show two RSAC designs that span the different design options and technology nodes. Simulations for the first design in a 130 nm process show energy savings of19x to 32x compared to a digital implementation for signal-to-quantization-noise ratios (SQNRs) of 30 dB to 24 dB, respectively. Simulations for the second design in a 28 nm FDSOI process show energy savings of 15.7x, 4x, 2.1x compared to a digital implementation running at the same sampling frequency for SQNRs of 8 dB, 14 dB and 20 dB, respectively. Finally, we present the design of an emotion recognition system composed solely of SAC-based dot-products. Based on the behavioral and energy models developed in this thesis, we obtain energy savings of 45% and 49% compared to a digital implementation for average probabilities of error of 0.23 and 0.07, running at frequencies of 1.87 MHz and 1.7 MHz, respectively.
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A dot product kernel using rapidly switched analog circuit