Georgia Institute of Technology has used analogue processing to squeeze a 3.12Top/W (average) artificial intelligence processor onto a CMOS (55nm) chip, consuming only 690μW (1.2V), and aimed at self-teaching micro-robots that need to learn about their immediate environments. The processor is an accelerator for ‘reinforcement learning’ – a behaviourist psychology-inspired learning algorithm that mimics the ...

This story continues at ISSCC: Analogue boost for AI robot processor

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