The Make Programming Simple Lab focuses on advancing computational methods and technologies to simplify programming and enhance system performance across diverse domains. Key research areas include Machine Learning Accelerators for efficient processing, Error Resilience techniques to ensure robust system operations, Intelligent Transportation solutions for smarter mobility, Multi-level Intermediate Representations for optimizing compiler designs, and Quantum Machine Learning for pioneering future-ready AI systems. The lab aims to bridge the gap between complex computational problems and practical, accessible programming solutions.
Primary Research Topics
Machine Learning Accelerator Design
Reliability
Intelligent Transportation Systems
Multi-level Intermediate Representation
Quantum Machine Learning
Supplementary Research Topics
Processor Idle Cycle Aggregation
GPU Computing
Cyber-Physical and IoT Systems
Scratchpad Memory
Software Branch Hinting
Coarse-Grain Reconfigurable Arrays
Power, Temperature and Variation aware Computing
Bypass Aware Compiler
Reduced bit-width Instruction Set Architecture
Real-Time Systems
LLM Applications
