Projects
Open Source Projects
Generative AI
- Diffusion Model
- Diffusion models are a class of generative models that learn to reverse a gradual noising process to produce high-quality data, such as images, from random noise. Inspired by probabilistic principles, they are increasingly popular in AI image generation. I have coded a basic diffusion model following the Denoising Diffusion Probabilistic Model (DDPM) paper, which is now publicly accessible.
https://github.com/JDan-16/Diffusion_model1
- Stable Diffusion
- Stable Diffusion is a generative model designed to create highly detailed images from textual input. It operates by progressively refining random noise into coherent visuals, leveraging diffusion techniques for efficient and realistic image synthesis. I have implemented the architecture of Stable Diffusion v1.5 in PyTorch.
https://github.com/JDan-16/Stable_Diffusion_v1_5
More open source projects coming soon...
Corporate Endeavors
Hexagon Tensor Processor
Company: Qualcomm
I contribute to the Hexagon Tensor Processor (HTP) team, focusing on deep learning operation kernels and optimizing model graphs, and writing backend IR code that integrates with the HTP graph compiler pipeline.
I design and implement Op Kernels using intrinsic SIMD programming with Hexagon Vector and Matrix extensions (HVX, HMX), and write hand-tuned assembly kernels (HLX) where required.
HTP is the neural processing unit (NPU) designed from the ground up for generative AI, integrated into all Snapdragon chipsets, including the Snapdragon X Series. This role requires me to stay current with state-of-the-art deep learning and generative AI models. To know more about HTP, see link.
Life Sciences Project
Company: Ceremorphic, Inc.
I have been responsible for architecting and designing a compiler from inception, tailored for analog compute circuits in the realm of life sciences applications.
Furthermore, I led a software team dedicated to constructing an AI-powered software framework tailored for in-house custom hardware within the life sciences domain. I developed proficiency in integrating diverse deep learning models, including Vision models, Language models (transformer, BERT), other Generative models (GAN, VAE), Graph based models (GCN), and more.
Deep Learning Accelerator
Company: Ceremorphic, Inc.
Inventing a power-efficient method, I optimized convolution and max-pool operations in a fused way, achieving 3-4x power efficiency without modifications during backpropagation—resulting in a filed patent. My role extended to graph-level and backend compiler optimizations, including operator kernels for an in-house neural processor and designing algorithms for quantization-aware training of neural networks, minimizing machine cycles.
AI powered Security and other Projects
Company: Ceremorphic, Inc.
Under the mentorship of Prof. Boris Murmann, I explored microarchitectural side-channel attacks (SCA) like Flush+Reload, Flush+Flush, Prime+Probe, etc., developing AI-inspired algorithms to mitigate such threats. My comprehensive efforts encompassed building and filing numerous intellectual properties (IPs) and patents. Other projects include exploration of fundamental quantum machine learning concepts and circuits.
Academic Projects
IIT Gandhinagar
M.Tech Thesis: An Approach towards Building Energy-Efficient Architectures for Neural Networks
My master’s thesis, “Energy-Efficient Architectures for Neural Networks,” advised by Prof. Joycee Mekie, focused on numerical representations for efficient and robust neural computation. I derived analytical bounds for the minimum exponent width in floating-point weight formats to preserve model accuracy, and established a mathematical relationship between mantissa precision and network depth, proposing techniques to improve resilience to bit-level errors.
Other minor projects include implementation of an elementary pipelined processor and a multilayer perceptron neural network on an FPGA platform.
Assam Engineering College
B.E. Final Year Project: Prediction of Water Usage Based on Weather Data Pattern Using Neural Network
My final year project, “Prediction of Water Usage Based on Weather Data Patterns Using Neural Networks,” supervised by Prof. Rashi Borgohain and Mr. Tanmoy Goswami, focused on data-driven resource optimization. The work involved building an embedded system using Raspberry Pi, integrating sensors for soil characteristics and water usage, and implementing a neural network for prediction. A web-based interface with RESTful APIs was developed for user interaction and monitoring.
Additionally, during an internship at IIT Guwahati, I worked on a machine learning–based human presence detection system using an AmigoBot, enabling autonomous navigation and litter collection upon detection.