Nitish Satya Murthy
Papers
1
Total Citations
2
H-Index
1
About
Nitish Satya Murthy is a rising researcher at the forefront of efficient edge computing for autonomous systems, with a particular focus on enabling real-time, on-device machine learning for robotics. His work addresses a critical bottleneck in modern robotics: the need for adaptive learning without reliance on cloud infrastructure. Murthy’s most notable contribution, detailed in his 2025 paper “Efficient Precision-Scalable Hardware for Microscaling (MX) Processing in Robotics Learning,” introduces a novel hardware architecture that leverages Microscaling (MX) data types. By intelligently combining integer and floating-point representations with shared exponents, his design dramatically reduces energy consumption during edge training—a key requirement for battery-powered robots operating in dynamic environments. While still early in his career, this work has already garnered attention (2 citations) for its practical approach to precision-scalable computing. Murthy’s research sits at the intersection of hardware design, machine learning, and robotics, promising to make autonomous systems more self-sufficient and energy-efficient. His focus on scalable, low-power solutions positions him as a promising voice in the future of intelligent, adaptive robotics.
Research Focus
Key Achievements
Top Papers
- 1