Arijit Raychowdhury
Papers
31
Total Citations
681
H-Index
15
About
Arijit Raychowdhury is a prominent researcher at the intersection of neuromorphic computing, robotics hardware, and energy-efficient machine learning architectures. His work addresses one of modern robotics' most pressing challenges: enabling autonomous, intelligent behavior within severe power and computational constraints at the edge. Raychowdhury's most influential contributions include pioneering bio-inspired computing platforms that bring reinforcement learning directly to autonomous micro-robots. His 55nm neuromorphic accelerator with stochastic synapses (72 citations) demonstrated that true machine autonomy could be achieved through hardware-embedded learning rather than offline training alone. Complementing this, his work on coupled oscillators for synchronized locomotion (90 citations) elegantly bridges biological central pattern generators with robotic gait control in hardware. His survey on FPGA-based robotic computing (112 citations) has become a key reference for the field, mapping the landscape of reconfigurable hardware solutions for robotics. He has further advanced mixed-signal and oscillator-based accelerators for SLAM — a fundamental navigation problem — and contributed to swarm robotics platforms and spiking neural network pipelines for edge devices. Collectively, Raychowdhury's research has shaped how the community approaches energy-efficient, brain-inspired hardware for autonomous systems, accumulating nearly 500 citations across these publications and establishing him as a leading voice in edge robotics and neuromorphic computing.
Research Focus
Key Achievements
Top Papers
- 1A Survey of FPGA-Based Robotic Computing112 citations · 2021
- 2Programmable coupled oscillators for synchronized locomotion90 citations · 2019
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- 10Analyzing and Improving Fault Tolerance of Learning-Based Navigation Systems22 citations · 2021