FPGA based Implementation of Binarized Neural Network for Sign Language Application

Jaiswal, Mohita; Sharma, Vaidehi; Sharma, Abhishek; Saini, Sandeep; Tomar, Raghuvir Singh

doi:10.1109/ises52644.2021.00077

Cited by 1 publication

(1 citation statement)

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“…They generated IP using HLS for deployment on an FPGA to recognize MNIST handwritten digits. In the study [30], they created their own dataset, the Indian Sign Language (ISL) Alphabet Dataset, and used the PYNQ-Z2 board with limited resources to implement sign language recognition using the Binarized Neural Network (BNN). However, their accuracy reached only 85%, indicating room for improvement.…”

Section: Results and Analysismentioning

confidence: 99%

Hardware accelerator for high accuracy sign language recognition with residual network based on FPGAs

Yang,

Li,

Hao

et al. 2024

IEICE Electron. Express

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The ResNet series of networks has demonstrated powerful capabilities in the fields of object detection and image classification, garnering increasing attention from researchers. However, due to their deep network architectures, accelerator development based on FPGA faces challenges associated with limited on-chip resources and lengthy design cycles. This paper presents a versatile hardware acceleration system based on FPGA, achieving optimization through both hardware implementation and software inference architecture. The system reduces network complexity by employing techniques such as inter-layer fusion and dynamic quantization, while enhancing hardware resource utilization through channel parallelism and tightly-pipelined hardware design principles. By configuring and reusing computation units, the forward inference process of ResNet series networks can be rapidly deployed on FPGA, shortening the development and validation cycles. The proposed system is validated using the ResNet-18 model on a PYNQ-Z2 development board within a gesture recognition application scenario. The overall power consumption of the system is 2.136W, with hardware inference accuracy reaching 98.87%.

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Section: Results and Analysismentioning

confidence: 99%