# Design of Quantized Deep Neural Network Hardware Inference Accelerator Using Systolic Architecture
> Rifqie D.M.

URL kanonis: https://discover.unhas.ac.id/publications/design-of-quantized-deep-neural-network-hardware-inference-accelerator-using-sys
Jurnal / Konferensi: Journal of Applied Science Engineering Technology and Education
Tahun terbit: 2024
DOI: https://doi.org/10.35877/454RI.asci2689
ISSN: 26850591
Citations: 0

## Authors
- Rifqie D.M.

## Abstract
This paper presents a hardware inference accelerator architecture of quantized deep neural networks (DNN). The proposed accelerator implements all computation in a quantize version of DNN including linear transformations like matrix multiplications, nonlinear activation functions such as ReLU, quantization and dequantization operation. The hardware accelerator of quantized DNN consists of matrix multiplication core which is implemented in systolic array architecture, and the QDR core for computing the operation of quantization, dequantization, and ReLU. This proposed hardware architecture is implemented in Verilog Hardware Description Language (HDL) code using modelsim. To validate, we simulated the quantized DNN using Python programming language and compared the results with our proposed hardware accelerator. The result of this comparison shows a very slight difference, confirming the validity of our quantized DNN hardware accelerator.

## Keywords
- Inference
- Computer science
- Systolic array
- Architecture
- Computer architecture
- Artificial neural network
- Hardware acceleration
- Computer hardware
- Embedded system
- Artificial intelligence
- Field-programmable gate array
- Very-large-scale integration
- Art
- Visual arts

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Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
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