ArticleMicromachines2023
Parameterizable Design on Convolutional Neural Networks Using Chisel Hardware Construction Language.
Article in Micromachines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- Flare: An FPGA-Based Full Precision Low Power CNN Accelerator with Reconfigurable Structure.Sensors (Basel, Switzerland) · 2024Article
- Editorial for the Beyond Moore's Law: Hardware Specialization and Advanced System on Chip.Micromachines · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper presents a parameterizable design generator on convolutional neural networks (CNNs) using the Chisel hardware construction language (HCL). By parameterizing structural designs such as the streaming width, pooling layer type, and floating point precision, multiple register-transfer level (RTL) implementations can be created to meet various accuracy and hardware cost requirements. The evaluation is based on generated RTL designs including 16-bit, 32-bit, 64-bit, and 128-bit implementations on field-programmable gate arrays (FPGAs). The experimental results show that the 32-bit design achieves optimal hardware performance when setting the same weights for estimating the quality of the results, FPGA slice count, and power dissipation. Although the focus is on CNNs, the approach can be extended to other neural network models for efficient RTL design.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.