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Paul Yi-Chia Chen

Publications and source records attributed to Paul Yi-Chia Chen.

2 recordsLinked to original sources

Chipstitch: A Scalable and Cost-Efficient Platform for Accelerated Silicon Prototyping

Silicon evidence serves as a critical receipt of credibility across academia and industry, validating innovation and signaling feasibility in ways that simulations alone cannot. Its value is difficult to quantify but widely recognized for driving hardware-software innovation. However, access to this receipt has remained severely constrained by high dollar costs and engineering effort barriers, which lock out innovators unable to afford it. To bridge this gap, we consider a new design paradigm called silicon query architectures that spans EDA, architecture, and VLSI and aims to transform single-die area into a substrate for scalable, tessellated, intra-die silicon receipts, dropping the cost of entry by one to two orders of magnitude without sacrificing research-grade specifications. We concretely propose Chipstitch, a silicon query architecture realized with three contributions: an algorithmic three-layer approach to repeatable and scalable tessellation with a keystone algorithm, a scalable architecture-VLSI system interconnect that fits within interstitial regions of the tessellation with research-grade timing and clocking, and a grid-aligned perimeter-based power shutdown technique that mitigates overheads for research-grade power measurement. Using a vertically integrated methodology extending down to post-layout VLSI implementation, we demonstrate Chipstitch on a 25-site heterogeneous design scenario, achieving 9.9x-14.5x total cost advantage and up to two orders of magnitude reduction in single-receipt entry cost compared to a traditional multi-project wafer baseline in Skywater 130nm and Intel 16nm process nodes.

cs.AR↗

PowerFlow-DNN: Compiler-Directed Fine-Grained Power Orchestration for End-to-End Edge AI Inference

Edge AI systems operate under stringent energy and volume constraints, demanding extreme efficiency on limited battery capacity, with requirements worsening as intelligent capabilities advance. Prior work suggests fine-grained power orchestration through DVFS and power gating significantly improves efficiency critical to meeting such constraints, but introduces new challenges. We observe that layer-level approaches incur unintended overheads due to inter-layer coupling of power-control decisions, and jointly managing these mechanisms under limited voltage rails and transition overheads leads to a rapidly growing combinatorial schedule space. We propose PowerFlow-DNN, a compiler-directed framework for end-to-end power-state orchestration in ultra-low-power accelerators. By constructing a rigorous problem formulation for deadline-constrained, real-time, periodic inference as a unified inter-layer power-scheduling problem, our framework discovers energy-minimal power-state schedules while accounting for inter-layer impacts. We evaluate the framework on a DNN accelerator VLSI implementation in TSMC 40nm technology. Across representative edge networks, our approach discovers near-optimal solutions and achieves energy within 0.04\% of the exact ILP oracle, reducing energy by up to 48\% compared to an aggressive baseline without power orchestration, while reasoning over a combinatorial schedule space of over $10^{160}$ possible power-state assignments, yet operating on a structured layered state graph that enables efficient optimization, achieving up to 2.14$\times$ solver speedup via lightweight pruning.

cs.AR↗