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Posts
Different implementations of the ubiquitous convolution
Published:
Throughout the Deep Learning field, nn.Conv2d is being used left-right-center for building efficient convolution layers in PyTorch without worrying much about how they are implemented under the hood. In this post, we will specifically gain some insights into different convolution implementations like a naive nested for-loop, Im2Col, Winograd, Strassen and FFT algorithms and infer their pros & cons based on latencies incurred on a N1 CPU and a T4 GPU. We will also relate Strassen’s algorithm to DeepMind’s recent computing breakthrough with AlphaTensor.
Unboxing ChatGPT: A Deep-Dive on How This AI-Driven Chatbot Was Trained
Published:
ChatGPT, OpenAI’s latest dialogue model, has taken the internet by storm, surpassing 1 million users in just 5 days. From seamless chatting to creating poetry and from writing code to conceiving an imaginary OS, its performance is truly mind-blowing. How did conversational AI become so much better so quickly? OpenAI appears to have cracked the nut using Reinforcement Learning with Human Feedback (RLHF) – a method that uses human demonstrations to guide the model toward desired behavior. In this article, we’ll unpack ChatGPT’s training techniques and take a deeper look at what goes on under the hood. Find the wandb article written by me here.
Augmented Reality, the reality disrupter
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Reality is becoming more and more elusive in our lives. Unless you have been living under a rock, you would have heard of Augmented Reality (AR), the technology that drives Snapchat filters, Pokemon Go, IKEA furniture place app etc. which superimposes a computer-generated image on a user’s real world view. This post is a personal take on the technological and business trends in the AR field. It might be the next biggest disrupter that seamlessly amplifies human capabilities.
awards
Talk 1 on Relevant Topic in Your Field
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portfolio
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Portfolio item number 2
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publications
Synapse, Systolic CNN Accelerator’s Mapper-Simulator Environment
Published in IIT Madras Bachelor Research Thesis presentation forum by Sundar Raman P, 2021
For ShaktiMAAN, an open-source systolic inference accelerator effort at RISE lab, I designed a python compiler that schedules instructions given network, architecture configuration, and an event-driven, analytical, data-flow accurate simulator. This infrastructure helped address challenges in hardware verification, bottleneck analysis, design-space trade-offs, and compiler optimization for our accelerator. Further, Deep Reinforcement Learning agents (using PPO optimization algorithm) were used along with mapper-simulator to evaluate and explore the design space (tunable hardware/software knobs like buffer-size, loop-order, etc.) of our hardware to map DL networks ∼10% more efficiently than existing heuristics on our hardware. Find slides, code.
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Perturbation Analysis of Practical Algorithms for Maximum Scatter TSP
Published in Symposium on Algorithm Engineering and Experiments (ALENEX22) by Sundar Raman P, Emil Biju, 2022
Proposed six simple-to-code, scalable heuristics for NP-hard Maximum Scatter Travelling Salesman Problem (MSTSP). Studied the reliability of these algorithms in terms of runtime, quality, and stability using smoothed analysis, by slightly perturbing the inputs. Observed practical efficacy of simple heuristics despite their exponential worst-case complexity due to polynomial expected runtime, as the worst-case instances are sparse and rare. Find code.
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Generating Drug-like Molecules from Gene Expression Signatures using Transformers
Published in Intelligent Systems for Molecular Biology (ISMB) by Sundar Raman P, Prashant G, 2022
Designed a modified transformer architecture to generate many drug-like molecules that can induce a desired transcriptomic profile based on gene-expression signatures. Outperformed then state-of-the-art 2-staged GAN model by ∼40% in validity, uniqueness, ∼30% in synthesizability, ∼10% in similarity metrics of generated molecules. Upon evaluating our model on unseen gene expression signatures (even disease-associated), we observed that the molecules generated by our model are not only similar to the actual compounds to a reasonable extent, but the model also learns certain structural and chemical features that are responsible for specific alterations in gene expression. Find full-paper, code.
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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