SYMPLE Lab

SYMPLE Lab (System-level Memory, Processor, and Logic Engineering) explores next-generation AI system design across the full stack — from algorithms to architectures to silicon. We develop memory-centric and precision-adaptive computing techniques for efficient large-scale AI. Our research targets emerging generative AI workloads, including large language and diffusion models. By tightly co-optimizing models and hardware, we push the limits of energy efficiency and scalability. Our goal is to bring powerful AI from the cloud closer to everyday devices.

We are Seeking Highly Motivated
Undergraduate & Graduate Students!

Research Goal: Cross-Stack Optimization for Efficient AI Systems

(Algorithm-Architecture-Circuit/Logic)

AI Semiconductor & SoC Chip

Silicon-level design of next-generation
AI accelerators for efficient
on-device intelligence.

AI System Architecture

Scalable and memory-centric architectures bridging models
and hardware platforms.

Algorithm Co-optimization

Cross-layer optimization of algorithms and hardware to maximize
efficiency and performance.

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