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Yu et al., 2024 - Google Patents

Case study: optimization methods with TVM hybrid-op on RISC-V packed SIMD

Yu et al., 2024

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Document ID
2185950162648510863
Author
Yu M
Yuan C
Chen T
Lee J
Publication year
Publication venue
IEEE Access

External Links

Snippet

In recent years, considerable research has focused on the use of custom hardware to accelerate deep learning on edge devices. However, the end-to-end flow of deep learning includes preprocessing and postprocessing. Deep learning hardware accelerators cannot …
Continue reading at ieeexplore.ieee.org (PDF) (other versions)

Classifications

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    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F8/00—Arrangements for software engineering
    • G06F8/40—Transformations of program code
    • G06F8/41—Compilation
    • G06F8/44—Encoding
    • G06F8/443—Optimisation
    • G06F8/4441—Reducing the execution time required by the program code
    • G06F8/4442—Reducing the number of cache misses; Data prefetching
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for programme control, e.g. control unit
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    • G06F9/30003—Arrangements for executing specific machine instructions
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    • G—PHYSICS
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    • G06F9/30—Arrangements for executing machine-instructions, e.g. instruction decode
    • G06F9/32—Address formation of the next instruction, e.g. incrementing the instruction counter, jump
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
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    • G06F9/00—Arrangements for programme control, e.g. control unit
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    • G06F7/38—Methods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation
    • G06F7/48—Methods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation using non-contact-making devices, e.g. tube, solid state device; using unspecified devices
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