BuzzSort: A Linear-Time, Event-Driven Data Conversion and Sorting Framework for Approximate Computing Architectures

Analog computational primitives, such as vector-matrix multipliers (VMMs), are foreseen to play a pivotal role in economizing computing; however, to improve the viability of general-purpose accelerators, there is a need for efficient data conversion and sorting during readout. This work introduces “BuzzSort,” an event-driven framework that simultaneously converts and sorts data from analog systems. BuzzSort acquires analog data, retrieves sorting indices, and produces a sorted output vector in linear time. We experimentally demonstrate and characterize the efficacy of BuzzSort with a field-programmable analog array (FPAA) in a 350 nm process and a field-programmable gate array (FPGA).