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About

Mihir Mody is a leading researcher at the intersection of embedded deep learning, computer vision, and industrial automation. His work focuses on enabling efficient deployment of Convolutional Neural Networks (CNNs) on resource-constrained devices, with applications spanning automotive, robotics, medicine, and industrial machine vision. Mody’s major contributions include pioneering dynamic and predictive quantization techniques for CNN inference, which minimize memory and computational overhead without sacrificing accuracy—a critical advancement for real-time embedded systems. He also developed a VLSI architecture achieving 1.2 TOPS for convolution layers, demonstrating hardware-level optimization for high-throughput neural networks. In the realm of heterogeneous computing, Mody proposed a novel OpenVX implementation that maximizes utilization across CPU, GPU, DSP, and hardware accelerators while minimizing latency. His work on a single-chip multi-axis servo drive for industrial systems further showcases his versatility, addressing precision motion control in robotics and conveyer belts. With over 20 citations across his most-cited papers, Mody’s research bridges theoretical innovation and practical deployment, making him a key figure in advancing efficient AI for edge and industrial applications.