Graduate Student Researcher | NanoCAD Lab, UCLA 2019 - 2024

Architecture design of photonic neural network accelerators, ongoing

• Work on system scaling analysis and parallelization strategies for free-space 4F optical neural network accelerators
• Design efficient architectures for on-chip photonic neural network accelerators with co-optimization of software and hardware (dataflow, reuse scheme, memory hierarchy, etc.)
• Develop custom performance simulators to estimate the system performance of different configurations
• Develop custom training functions with integrated hardware simulation models for different types of photonic neural networks

Deep learning on microcontrollers, completed

• Proposed a software framework to compress and accelerate arbitrary precision neural networks on resource-constrained microcontrollers based on weight sharing, bit-serial computation, and lookup tables
• Designed and implemented a microcontroller runtime library for the proposed framework
• Implemented custom PyTorch functions for training neural networks with various weight-sharing techniques

Low-power deep learning accelerator design, completed

• Proposed hardware accelerator architecture based on systolic array for low-power neural network inference that leverages bit-level sparsity
• Analyzed various trade-off between system performance and power efficiency
• Co-designed the software and hardware to achieve optimal efficiency-accuracy trade-off
• Implemented custom PyTorch functions for training neural networks with bit-level sparsity

Summer Researcher | Custom Computing Group, Imperial College London Summer 2017

• Conducted research on accelerating quantum chemistry simulation with FPGA-based data flow engines
• Wrote C codes to parallelize Monte Carlo simulation on data flow engines
• Conducted performance bottleneck analysis and optimized the algorithm accordingly

Machine Learning Performance Engineer | Waymo 2024 - Present

• Optimize the performance of machine learning models across the stack

Summer Intern | Qualcomm Summer 2023

• Worked on performance modeling and optimization of large language model (LLM) workloads on the AIC 100 inference accelerator.
• Designed and implemented a memory simulator that models SRAM usage, memory spilling, and total DDR traffic for large language models.
• Proposed several memory optimization methods to mitigate memory spilling and reduce total DDR traffic as model size, sequence length, and batch size increase.

Summer Intern | Qualcomm Summer 2020

• Participated in a neural network accelerator design project and worked on the middleware design and development
• Developed a complete microcontroller program to control hardware modules and handle various execution modes
• Designed control flow to handle communication and synchronization between different hardware modules and guarantee correct execution sequence

Organizer | UCLA Computer Engineering Bootcamp Summer 2022

• Organized a comprehensive 5-day bootcamp program for incoming UCLA computer engineering freshmen, aimed at equipping them with the necessary skills and knowledge to excel in their future studies and campus life
• Developed and curated structured modules for the bootcamp and find suitable instructors to deliver high-quality instruction across all modules
• Taught the Machine Learning module and provided students with hands-on lab sessions to practice their skills

Instructor | Los Angeles Computing Circle (LACC) Summer 2021, Summer 2022

• Volunteered at LACC for two consecutive years, which is a free summer program for high school students with the objective of engaging and mentoring younger students for careers in computing and engineering
• Taught the Machine Learning and Artificial Intelligence Module and provided students with hands-on lab sessions to practice their skills