| Naslov: | Towards deploying highly quantized neural networks on FPGA using chisel |
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| Avtorji: | ID Vreča, Jure, Institut Jožef Stefan (Avtor)
ID Biasizzo, Anton, Institut Jožef Stefan (Avtor) |
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| Datoteke: |  URL - Izvorni URL, za dostop obiščite https://ieeexplore.ieee.org/document/10456782/authors#authors
 PDF - Predstavitvena datoteka, prenos (419,83 KB)
MD5: C6531548B85A232A34C012D82828C084
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| Jezik: | Angleški jezik |
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| Tipologija: | 1.08 - Objavljeni znanstveni prispevek na konferenci |
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| Organizacija: |  IJS - Institut Jožef Stefan
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| Povzetek: | We present chisel4ml, a Chisel-based tool that generates hardware for highly quantized neural networks described in QKeras. Such networks typically use parameters with bitwidths less than 8 bits and may have pruned connections. Chisel4ml can generate the highly quantized neural network as a single combinational circuit with pipeline registers in between the different layers. It supports heterogeneous quantization where each layer can have a different precision. The full parallelization enables very low-latency and high throughput inference, that are required for certain tasks. We illustrate this on the triggering system for the CERN Large Hadron Collider, which filters out events of interest and sends them on for further processing. We compare our tool against hls4ml, a high-level synthesis based approach for deploying similar neural networks. Chisel4ml is still under development. However, it already achieves comparable results to hls4ml for some neural network architectures. Chisel4ml is available on https://github.com/cs-jsi/chisel4ml. |
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| Ključne besede: | neural networks, QKeras, Chisel4ml |
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| Status publikacije: | Objavljeno |
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| Verzija publikacije: | Recenzirani rokopis |
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| Datum objave: | 19.03.2023 |
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| Založnik: | IEEE |
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| Leto izida: | 2023 |
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| Št. strani: | Str. 161-167 |
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| Izvor: | ZDA |
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| PID: | 20.500.12556/DiRROS-18802  |
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| UDK: | 004 |
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| DOI: | 10.1109/DSD60849.2023.00032 |
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| COBISS.SI-ID: | 190218499 |
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| Avtorske pravice: | ©2023 IEEE |
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| Opomba: | Nasl. z nasl. zaslona;
Opis vira z dne 25. 3. 2024;
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| Datum objave v DiRROS: | 23.04.2024 |
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| Število ogledov: | 381 |
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| Število prenosov: | 244 |
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| Metapodatki: |    |
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