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About

Kaushik Roy is a prominent researcher at the intersection of neuromorphic computing, brain-inspired architectures, and energy-efficient hardware for artificial intelligence. His work spans several interconnected domains, including spiking neural networks (SNNs), compute-in-memory (CIM) systems, event-based sensing, and robotic perception, making him a versatile and influential voice in next-generation computing research. Roy's most-cited contributions include foundational analyses of Liquid State Machines for bio-inspired sequence processing (55 citations) and landmark investigations into compute-in-memory architectures that address the memory-bottleneck challenge in deep learning workloads (41 citations each). These works have helped shape how the community approaches efficient hardware design for machine learning accelerators. His more recent research pushes these ideas into real-world robotics applications, notably through neuromorphic event cameras for optical flow estimation, energy-efficient navigation planners, and in-sensor computing that eliminates costly analog-to-digital conversion. Collectively accumulating over 200 citations, Roy's research demonstrates a consistent commitment to bridging biological neural principles with practical, low-power computing systems. His perspective on neuromorphic computing for robotic vision and lifelong learning further reflects a forward-looking agenda, positioning his work as essential reading for students and researchers pursuing sustainable, brain-inspired AI solutions.