Bharadwaj Krishnamurthy
Papers
1
Total Citations
43
H-Index
1
About
Bharadwaj Krishnamurthy is a leading researcher at the intersection of deep learning and embedded systems, with a primary focus on the efficient deployment of deep convolutional neural networks (DCNNs) on resource-constrained mobile platforms. His most cited work, "An efficient implementation of deep convolutional neural networks on a mobile coprocessor" (2014, 43 citations), addresses the critical challenge of managing the hundreds of intermediate results generated by DCNNs, which often overwhelm mobile processors. By proposing a hardware-accelerated, real-time implementation using a coprocessor, Krishnamurthy demonstrated that complex neural networks can run effectively on low-power devices without sacrificing performance. This contribution is foundational for enabling on-device AI applications, from real-time image recognition to augmented reality, where latency and energy efficiency are paramount. His research bridges the gap between high-accuracy deep learning models and practical mobile deployment, earning recognition for its direct impact on edge computing. Krishnamurthy’s work continues to influence the design of efficient neural network architectures and hardware-software co-design, making him a key figure in bringing AI to everyday mobile technology.
Research Focus
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Top Papers
- 1