ADAS/AI Software Engineer:

Working with the Qualcomm Automotive team on ADAS AI model quantization, performance optimization, and deployment across automotive and embedded platforms. Responsibilities include onboarding and deploying CNNs, Vision Transformers, LLMs (Llama-3, Phi-3.5, Qwen), and diffusion models on Qualcomm SoCs; building mixed-precision (INT8/FP8/FP16) inference pipelines with operator fusion to reduce latency and energy; collaborating with hardware and runtime teams to optimize model-to-accelerator mappings under real-time ADAS constraints; benchmarking deployments using Qualcomm AI Engine, TensorRT, and ONNX Runtime; integrating perception models such as object detection, lane segmentation, and driver monitoring into production ADAS pipelines in compliance with ISO 26262; and automating end-to-end deployment workflows using Docker, CI/CD, and Python toolchains.

Graduate Research Assistant:

I am working on deep learning model optimization techniques for low power perception for autonomous vehicles at the EPiC lab at Colorado state university under the guidance of Dr. Sudeep Pasricha. As a part of my research, I have been the Technical Advisor of the Connected and Autonomous vehicles system for the CSU vehicle innovation team for ECOCAR challenge.

Graduate Teaching Assistant:

I am the GTA for ECE 452 Computer Architecture and Organization taught by Dr. Sudeep Pasricha. I help in preparing course material, Assignments, resolving doubts and queries of students, and grading the course. The course consists of a total of 54 students.

Object Detection Model Compression for Resource constraint platform:

Recreated the SSD object detection model on using TensorFlow and Keras (not using TF object detection API). Worked on mixed-precision pruning, iterative layer by layer pruning and quantization. I trained this model as a graph using custom loss function and gradient function. Using train as a function.

ROS Based Stereo Vision System for Autonomous Navigation:

A Stereo vision based autonomous navigation system that used Deep learning model for Object Detection and Navigation. ROS For communication and an android app with ROS backend for GPS data acquisition.

CAVS Technical Advisor:

As a part of CSU vehicle innovation team for Ecocar Mobility challenge is a competition in which we develop a prototype of Level 2 automotive vehicle. My role as a graduate student is to come up with ideas that can improve the CAVS system performance.

Selected publications and research contributions. Full list available on Google Scholar

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1. UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles Abhishek Balasubramaniam, Febin P Sunny, Sudeep Pasricha
   2025 Design, Automation & Test in Europe Conference (DATE) | Google Scholar
2. R-TOSS: A Framework for Real-Time Object Detection Using Semi-Structured Pruning Abhishek Balasubramaniam, Febin Sunny, Sudeep Pasricha
   2023 60th ACM/IEEE Design Automation Conference (DAC) | Google Scholar
3. Object Detection in Autonomous Cyber-Physical Vehicle Platforms: Status and Open Challenges Abhishek Balasubramaniam, Sudeep Pasricha
   Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems | Google Scholar
4. Object Detection in Autonomous Vehicles: Status and Open Challenges A Balasubramaniam, S Pasricha
   arXiv preprint, 2022 | Google Scholar
5. Opima: Optical Processing-in-Memory for Convolutional Neural Network Acceleration Febin Sunny, Amin Shafiee, Abhishek Balasubramaniam, Mahdi Nikdast, Sudeep Pasricha
   IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems | Google Scholar
6. ROS-Based Stereo Vision System for Autonomous Vehicles Abhishek Balasubramaniam, S Gautham, D Varun Rufus Raj Samuel, K Keshav, UP Vignesh, Shyam R Nair
   2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI) | Google Scholar
7. Low-Cost ROS-Based Semi-Autonomous Drone with Position and Altitude Lock Abhishek Balasubramaniam, K Keshav, S Gautham, D Varun Rufus Raj Samuel, Shyam R Nair
   2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI) | Google Scholar
8. Optimizing Machine Learning Models for Autonomous Vehicles Abhishek Balasubramaniam
   Colorado State University | Google Scholar