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1.
Hardware–software co-design of an audio feature extraction pipeline for machine learning applications
Jure Vreča, Ratko Pilipović, Anton Biasizzo, 2024, izvirni znanstveni članek

Povzetek: Keyword spotting is an important part of modern speech recognition pipelines. Typical contemporary keyword-spotting systems are based on Mel-Frequency Cepstral Coefficient (MFCC) audio features, which are relatively complex to compute. Considering the always-on nature of many keyword-spotting systems, it is prudent to optimize this part of the detection pipeline. We explore the simplifications of the MFCC audio features and derive a simplified version that can be more easily used in embedded applications. Additionally, we implement a hardware generator that generates an appropriate hardware pipeline for the simplified audio feature extraction. Using Chisel4ml framework, we integrate hardware generators into Python-based Keras framework, which facilitates the training process of the machine learning models using our simplified audio features.
Ključne besede: FPGA, MFCC, keyword spotting, chisel
Objavljeno v DiRROS: 25.03.2024; Ogledov: 321; Prenosov: 371
.pdf Celotno besedilo (1,05 MB)
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2.
A configurable mixed-precision convolution processing unit generator in Chisel
Jure Vreča, Anton Biasizzo, 2023, objavljeni znanstveni prispevek na konferenci

Ključne besede: neural networks, quantization, Chisel, FPGA
Objavljeno v DiRROS: 08.06.2023; Ogledov: 373; Prenosov: 237
.pdf Celotno besedilo (332,90 KB)
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