Tzung-Han Juang

SkeleShare: Algorithmic Skeletons and Equality Saturation for Hardware Resource Sharing

Compiling functional programs into efficient Field Programmable Gate Array (FPGA) designs is difficult. Hardware resources must be explicitl… (voir plus)y allocated and shared to maximize resource efficiency. This requires careful orchestration of several transformations to expose and exploit sharing opportunities.This paper introduces SkeleShare, a novel approach that automates the problem of resource allocation and sharing. It leverages equality saturation and algorithmic skeletons to expose sharing opportunities across abstraction levels. A solver-based extractor then selects a design that consolidates computations, meeting resource constraints while maintaining performance.This approach is evaluated on neural networks and image processing targeting a real FPGA. The paper shows how SkeleShare is used to express the various algorithmic patterns and transformation rules inherent in neural network operators. The experimental evaluation demonstrates that SkeleShare’s fully automated resource allocation and sharing matches and exceeds the performance of prior work, which involves expert manual extraction of sharing opportunities.

2026-01-30

IEEE/ACM Symposium on Code Generation and Optimization (publié)

doi.org

Maximizing Data and Hardware Reuse for HLS with Early-Stage Symbolic Partitioning.

Tzung-Han Juang

Christophe Dubach

While traditional High-Level Synthesis (HLS) converts “high-level” C-like programs into hardware automatically, producing high-performan… (voir plus)ce designs still requires hardware expertise. Optimizations such as data partitioning can have a large impact on performance since they directly affect data reuse patterns and the ability to reuse hardware. However, optimizing partitioning is a difficult process since minor changes in the parameter choices can lead to totally unpredictable performance. Functional array-based languages have been proposed instead of C-based approaches, as they offer stronger performance guarantees. This article proposes to follow a similar approach and exposes a divide-and-conquer primitive at the algorithmic level to let users partition any arbitrary computation. The compiler is then free to explore different partition shapes to maximize both data and hardware reuse automatically. The main challenge remains that the impact of partitioning is only known much later in the compilation flow. This is due to the hard-to-predict effects of the many optimizations applied during compilation. To solve this problem, the partitioning is expressed using a set of symbolic tunable parameters, introduced early in the compilation pipeline. A symbolic performance model is then used in the last compilation stage to predict performance based on the possible values of the tunable parameters. Using this approach, a design space exploration is conducted on an Intel Arria 10 Field Programmable Gate Arrays (FPGAs), and competitive performance is achieved on the classical VGG and TinyYolo neural networks.

2024-12-31

ACM Trans. Archit. Code Optim. (publié)

doi.org

Let Coarse-Grained Resources Be Shared: Mapping Entire Neural Networks on FPGAs

Tzung-Han Juang

Christof Schlaak

Christophe Dubach

2023-09-08

ACM Transactions on Embedded Computing Systems (publié)

doi.org

Memory-Aware Functional IR for Higher-Level Synthesis of Accelerators

Christof Schlaak