Jie Han

Multiplier Optimization by Exploring Logical Correlations Among Partial Products

EDISCO: Equivariant DIScrete Diffusion for Euclidean Combinatorial Optimization

Euclidean combinatorial optimization problems (ECOPs), such as the Traveling Salesman Problem (TSP) and Capacitated Vehicle Routing Problem (CVRP), possess inherent symmetries under the two-dimensional Euclidean group E(2), including rotations, reflections, and translations. Existing learning-based methods, including r...

Adaptive Multiply-Accumulate Unit with Scalable Critical Path for Aggressive Voltage Underscaling DNN Accelerators

PPAPlace: Differentiable Cross-Stage Objectives for Chip Placement Optimization

Macro placement significantly affects a chip's post-route performance, power, and area (PPA). Most placement methods optimize half-perimeter wirelength (HPWL) as the primary objective. However, recent benchmarking shows a near-zero correlation between HPWL and post-route timing metrics such as the worst negative slack...

HAP: Efficient Quantization Harnessing Adaptive Precision for DNN Hardware Acceleration

A novel quantization and accelerator design harnessing adaptive-precision (HAP), leverages a per-group dynamic zero-point to generalize APQ and achieves superior accuracy compared with existing PTQ solutions at the level of 4 or 5 bits for a variety of DNN families.

CoEvoP&R: Co-Evolving Placement Objectives with Routing Feedback via Large Language Models

CoEvoP&R addresses limitations with a large language model (LLM)-based framework that automatically evolves analytical placement objectives and reduces post-route routed wirelength and congestion across eight ChiP-Bench Nangate45 designs and three seeds.