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Sabrina Yarzada

Publications and source records attributed to Sabrina Yarzada.

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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

Capstone: Power-Capped Pipelining for Coarse-Grained Reconfigurable Array Compilers

Coarse-grained reconfigurable arrays (CGRAs) have attracted growing interest because they exhibit performance and energy efficiency competitive with ASICs while maintaining flexibility similar to FPGAs. These properties make CGRAs attractive in accelerator and other power-constrained system contexts. However, modern CGRA compilers aggressively pipeline for frequency and performance improvements, often violating hard power budgets. We empirically show that, in state-of-the-art CGRA compilers such as Cascade, post-place-and-route (post-PnR) pipelining increases power monotonically and ultimately exceeds fixed power caps across diverse workloads. In response, we introduce \emph{Capstone}, a power-aware extension of Cascade that integrates a fast, compiler-resident power model with a user-tunable controller that guides the bitstream selection process towards optimization targets. Capstone predicts per-iteration power directly inside the post-PnR compilation loop and selects one or a small set of PnR configurations such that at least one meets a user-specified power cap. Thus, we shift the objective from indiscriminately maximizing frequency to maximizing safe frequency under a discrete power cap. On a suite of kernels spanning fundamental dense and sparse applications, Capstone meets a power cap and minimizes remaining power headroom while preserving feasible performance. Our results indicate that cap-aware compilation is both necessary and practical, as the compiler can proactively land on cap-compliant points and expose predictable performance under power constraints.

cs.AR