Adaptive CMOS: from biological inspiration to systems-on-a-chip

Abstract

Local long-term adaptation is a well-known feature of the synaptic junctions in nerve tissue. Neuroscientists have demonstrated that biology uses local adaptation both to tune the performance of neural circuits and for long-term learning. Many researchers believe it is key to the intelligent behavior and the efficiency of biological organizms. Although engineers use adaptation in feedback circuits and in software neural networks, they do not use local adaptation in integrated circuits to the same extent that biology does in nerve tissue. A primary reason is that locally adaptive circuits have proved difficult to implement in silicon. We describe complementary metal-oxide-semiconductor (CMOS) devices called synapse transistors that facilitate local long-term adaptation in silicon. We show that synapse transistors enable self-tuning analog circuits in digital CMOS, facilitating mixed-signal systems-on-a-chip. We also show that synapse transistors enable silicon circuits that learn autonomously, promising sophisticated learning algorithms in CMOS.