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Emerging sensing applications create an unprecedented need for energy efficiency in programmable processors. To achieve useful multi-year deployments on a small battery or energy harvester, these applications must avoid off-device communication and instead process most data locally. Recent work has proven coarse-grained reconfigurable arrays (CGRAs) as a promising architecture for this domain. Unfortunately, nearly all prior CGRAs support only computations with simple control flow and no memory aliasing (e.g., affine inner loops), causing an Amdahl efficiency bottleneck as non-trivial fractions of programs must run on an inefficient von Neumann core.RipTide is a co-designed compiler and CGRA architecture that achieves both high programmability and extreme energy efficiency, eliminating this bottleneck. RipTide provides a rich set of control-flow operators that support arbitrary control flow and memory access on the CGRA fabric. RipTide implements these primitives without tagged tokens to save energy; this requires careful ordering analysis in the compiler to guarantee correctness. RipTide further saves energy and area by offloading most control operations into its programmable on-chip network, where they can re-use existing network switches. RipTide’s compiler is implemented in LLVM, and its hardware is synthesized in Intel 22FFL. RipTide compiles applications written in C while saving 25% energy v. the state-of-the-art energy-minimal CGRA and 6.6 × energy v. a von Neumann core.
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APEX: A Framework for Automated Processing Element Design Space Exploration using Frequent Subgraph Analysis
The architecture of a coarse-grained reconfigurable array (CGRA) processing element (PE) has a significant effect on the performance and energy-efficiency of an application running on the CGRA. This paper presents APEX, an automated approach for generating specialized PE architectures for an application or an application domain. APEX first analyzes application domain benchmarks using frequent subgraph mining to extract commonly occurring computational subgraphs. APEX then generates specialized PEs by merging subgraphs using a datapath graph merging algorithm. The merged datapath graphs are translated into a PE specification from which we automatically generate the PE hardware description in Verilog along with a compiler that maps applications to the PE. The PE hardware and compiler are inserted into a flexible CGRA generation and compilation toolchain that allows for agile evaluation of CGRAs. We evaluate APEX for two domains, machine learning and image processing. For image processing applications, our automatically generated CGRAs with specialized PEs achieve from 5% to 30% less area and from 22% to 46% less energy compared to a general-purpose CGRA. For machine learning applications, our automatically generated CGRAs consume 16% to 59% less energy and 22% to 39% less area than a general-purpose CGRA. This work paves the way for creation of application domain-driven design-space exploration frameworks that automatically generate efficient programmable accelerators, with a much lower design effort for both hardware and compiler generation.
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- Award ID(s):
- 2238006
- PAR ID:
- 10493552
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9781450399180
- Page Range / eLocation ID:
- 33 to 45
- Format(s):
- Medium: X
- Location:
- Vancouver BC Canada
- Sponsoring Org:
- National Science Foundation
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