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A core problem in hardware-software codesign is in the sheer size of the design space. Without a set ISA to constrain the hardware-software interface, the design space explodes. This work presents a strategy for managing the massive hardware-software design space within the domain of machine learning inference workloads and accelerators. We first propose EngineIR, a new language for representing machine learning hardware and software in a single program. Then, using equality graphs -- a data structure from the compilers literature -- we suggest a method for efficiently enumerating the design space by performing rewrites over our representation.
In recent years the computing landscape has seen an in- creasing shift towards specialized accelerators. Field pro- grammable gate arrays (FPGAs) are particularly promising as they offer significant performance and energy improvements compared to CPU
This volume contains the formal proceedings of the 4th International Workshop on Rewriting Techniques for Program Transformations and Evaluation (WPTE 2017), held on 8th September 2017 in Oxford, United Kingdom, and affiliated with the Second Interna
Memory disaggregation has attracted great attention recently because of its benefits in efficient memory utilization and ease of management. So far, memory disaggregation research has all taken one of two approaches, building/emulating memory nodes w
PLACES 2016 (full title: Programming Language Approaches to Concurrency- and Communication-Centric Software) is the ninth edition of the PLACES workshop series. After the first PLACES, which was affiliated to DisCoTec in 2008, the workshop has been p
Personalized PageRank (PPR) is a graph algorithm that evaluates the importance of the surrounding nodes from a source node. Widely used in social network related applications such as recommender systems, PPR requires real-time responses (latency) for