Seth Bridges, Miguel Figueroa, Chris Diorio, David Hsu
This paper introduces the Field-Programmable Learning Array, a new paradigm for rapid prototyping of learning primitives and machine- learning algorithms in silicon. The FPLA is a mixed-signal counterpart to the all-digital Field-Programmable Gate Array in that it enables rapid prototyping of algorithms in hardware. Unlike the FPGA, the FPLA is targeted directly for machine learning by providing local, parallel, on- line analog learning using ﬂoating-gate MOS synapse transistors. We present a prototype FPLA chip comprising an array of reconﬁgurable computational blocks and local interconnect. We demonstrate the via- bility of this architecture by mapping several learning circuits onto the prototype chip.