team

Nitin Chandrachoodan

Professor

PhD  from the University of Maryland at College Park in 2002

044-2257 4432

nitin@ee.iitm.ac.in

  • I am a professor in the department of Electrical Engineering at IIT Madras. My undergraduate degree was in Electronics and Communication Engineering from the same department in 1996, followed by a PhD from the University of Maryland at College Park in 2002.
  • I primarily work in the areas of digital system design and VLSI / FPGA implementations of DSP systems, and am part of the Integrated Circuits and Systems Group at IIT Madras.

  • Approximate computing.
  • Low-power signal processing.
  • Architectures for machine learning and techniques for low power/low complexity computation.

    Previous Courses

  • EE 2003 - Computer Organization. Videos from Aug 2020 are on YouTube.
  • EE 5332 - Mapping DSP Algorithms to Architectures (Jan 2019) - unedited videos of the lectures are available at YouTube.
  • EE 5311 - Digital IC Design (multiple times).
  • EE 4371 - Data Structures and Algorithms (Aug 2018).
  • EE 5703 - FPGA lab There was previously material linked here but it is out of date.
  • EE2003 (Computer Organization) in Aug 2021.

  • Optimization with photonic wave-based annealers A Prabhakar, P Shah, U Gautham, V Natarajan, V Ramesh, Philosophical Transactions of the Royal Society A 381 (2241), 20210409 2023.
  • Split-Knit Convolution: Enabling Dense Evaluation of Transpose and Dilated Convolutions on GPUs AM Vadakkeveedu, D Mandal, P Ramachandran, N Chandrachoodan 2022 IEEE 29th International Conference on High Performance Computing, Data 2022.
  • Snoopy: A Webpage Fingerprinting Framework with Finite Query Model for Mass-Surveillance G Mitra, PK Vairam, S Saha, N Chandrachoodan, V Kamakoti IEEE Transactions on Dependable and Secure Computing 2022.
  • Layerwise Disaggregated Evaluation of Spiking Neural Networks A Nallathambi, S Sen, A Raghunathan, N Chandrachoodan Proceedings of the ACM/IEEE International Symposium on Low Power Electronics 2022.
  • Reduced Memory Viterbi Decoding for Hardware-accelerated Speech Recognition PP Raj, PA Reddy, N Chandrachoodan ACM Transactions on Embedded Computing Systems (TECS) 21 (3), 2022.
  • A Smoothed LASSO-Based DNN Sparsification Technique BNG Koneru, N Chandrachoodan, V Vasudevan IEEE Transactions on Circuits and Systems I: Regular Papers 68 (10), 4287-4298 2021.
  • Benchmarking the Poor Man's Ising Machine G Umasankar, PS Shah, N Chandrachoodan, A Prabhakar 2021 IEEE Photonics Society Summer Topicals Meeting Series (SUM), 2021.
  • Probabilistic spike propagation for efficient hardware implementation of spiking neural networks A Nallathambi, S Sen, A Raghunathan, N Chandrachoodan Frontiers in Neuroscience 15, 694402 2021.
  • Analysis of power - accuracy trade off in digital signal processing applications using low power approximate adders C Dharmaraj, V Vasudevan, N Chandrachoodan IET Computers & Digital Techniques 15 (2), 2021.
  • Optimization of signal processing applications using parameterized error models for approximate adders C Dharmaraj, V Vasudevan, N Chandrachoodan ACM Transactions on Embedded Computing Systems (TECS) 20 (2), 2021.
  • Energy Reduction in Turbo Decoding through Dynamically Varying Bit-Widths S Rangachari, N Chandrachoodan 2020 IEEE International Symposium on Circuits and Systems (ISCAS),2020.

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