Analog and Mixed Signal Circuits and Systems for Emerging Applications

Quantum Computing, having the ability to exponentially enhance the raw computing power, and Artificial Intelligence (AI), having the ability to impart unprecedented intelligence to connected devices through algorithms that learn, are the two key technologies of the 21st century. Although novel devices can significantly advance the field of quantum computing, conventional CMOS based analog and mixed signal circuits can enable quantum computing using classical op amp based circuits. AI algorithms on the other hand are tolerant to errors in computation, thereby enabling approximate and low-precision computing which has resulted in the resurrection of more than half-a-century old analog computing.

This talk would present various analog computing techniques that could enable AI algorithms or more specifically machine learning (ML) algorithms. The talk could also delve into mixed-sginal computing where it will dive into the details of in-memory computing as well as adoption of novel memory devices, viz., memristors, to enable energy efficient computing. The talk would also present some classical analog hardware for emulating quantum algorithms, like Grover’s search algorithm, for quantum computing systems.