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Paul Chen

Publications and source records attributed to Paul Chen.

3 recordsLinked to original sources

Algorithm-Driven On-Chip Integration for High Density and Low Cost

Growing interest in semiconductor workforce development has generated demand for platforms capable of supporting large numbers of independent hardware designs for research and training without imposing high per-project overhead. Traditional multi-project wafer (MPW) services based solely on physical co-placement have historically met this need, yet their scalability breaks down as project counts rise. Recent efforts towards scalable chip tapeouts mitigate these limitations by integrating many small designs within a shared die and attempt to amortize costly resources such as IO pads and memory macros. However, foundational principles for arranging, linking, and validating such densely integrated design sites have received limited systematic investigation. This work presents a new approach with three key techniques to address this gap. First, we establish a structured formulation of the design space that enables automated, algorithm-driven packing of many projects, replacing manual layout practices. Second, we introduce an architecture that exploits only the narrow-area regions between sites to deliver on off-chip communication and other shared needs. Third, we provide a practical approach for on-chip power domains enabling per-project power characterization at a standard laboratory bench and requiring no expertise in low-power ASIC design. Experimental results show that our approach achieves substantial area reductions of up to 13x over state-of-the-art physical-only aggregation methods, offering a scalable and cost-effective path forward for large-scale tapeout environments.

cs.AR

The Art of Tool Interface Design

We present an agentic framework, Thinker, which achieves state of art performance in challenging reasoning tasks for realistic customer service scenarios that involve complex business logic and human interactions via long horizons. On the $\tau$-bench retail dataset, Thinker achieves 82.6\% success rate with GPT-4o (version 2024-06-01) (baseline: 68.3\%), and 81.9\% success rate with Llama-3.1 405B (baseline: 49.6\%), without any fine-tuning. Thinker effectively closes the gap in reasoning capabilities between the base models by introducing proper structure. The key features of the Thinker framework are: (1) State-Machine Augmented Generation (SMAG), which represents business logic as state machines and the LLM uses state machines as tools. (2) Delegation of tasks from the main reasoning loop to LLM-powered tools. (3) Adaptive context management. Our prompting-only solution achieves signficant gains, while still maintaining a standard agentic architecture with a ReAct style reasoning loop. The key is to innovate on the tool interface design, as exemplified by SMAG and the LLM-powered tools.

cs.AI

Exploiting On-chip Heterogeneity of Versal Architecture for GNN Inference Acceleration

Graph Neural Networks (GNNs) have revolutionized many Machine Learning (ML) applications, such as social network analysis, bioinformatics, etc. GNN inference can be accelerated by exploiting data sparsity in the input graph, vertex features, and intermediate data in GNN computations. For dynamic sparsity exploitation, we leverage the heterogeneous computing capabilities of AMD Versal ACAP architecture to accelerate GNN inference. We develop a custom hardware module that executes the sparse primitives of the computation kernel on the Programmable Logic (PL) and efficiently computes the dense primitives using the AI Engine (AIE). To exploit data sparsity during inference, we devise a runtime kernel mapping strategy that dynamically assigns computation tasks to the PL and AIE based on data sparsity. Our implementation on the VCK5000 ACAP platform leads to superior performance compared with the state-of-the-art implementations on CPU, GPU, ACAP, and other custom GNN accelerators. Compared with these implementations, we achieve significant average runtime speedup across various models and datasets of 162.42x, 17.01x, 9.90x, and 27.23x, respectively. Furthermore, for Graph Convolutional Network (GCN) inference, our approach leads to a speedup of 3.9-96.7x compared to designs using PL only on the same ACAP device.

cs.AR