SNAPE-FP: SqueezeNet CNN with Accelerated Pooling Layers Extension based on IEEE-754 Floating Point Implementation through SW/HW Partitioning On ZYNQ SoC

Abotaleb, Abdelrhman M.; Ahmed, Mohab H.; Fathi, Mazen A.

doi:10.1109/niles53778.2021.9600528

Cited by 2 publications

(1 citation statement)

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“…Many studies on accelerating SqueezeNet on FPGA have been introduced in the literature [ 35 , 36 , 37 , 38 , 39 ]. In [ 35 ], the authors attempted to enhance the performance of the hardware processing convolution operation through pipelining and loop unrolling and flattening.…”

Section: Introductionmentioning

confidence: 99%

Lightweight and Energy-Efficient Deep Learning Accelerator for Real-Time Object Detection on Edge Devices

Jang

Park

Lee

et al. 2023

Sensors

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Tiny machine learning (TinyML) has become an emerging field according to the rapid growth in the area of the internet of things (IoT). However, most deep learning algorithms are too complex, require a lot of memory to store data, and consume an enormous amount of energy for calculation/data movement; therefore, the algorithms are not suitable for IoT devices such as various sensors and imaging systems. Furthermore, typical hardware accelerators cannot be embedded in these resource-constrained edge devices, and they are difficult to drive real-time inference processing as well. To perform the real-time processing on these battery-operated devices, deep learning models should be compact and hardware-optimized, and hardware accelerator designs also have to be lightweight and consume extremely low energy. Therefore, we present an optimized network model through model simplification and compression for the hardware to be implemented, and propose a hardware architecture for a lightweight and energy-efficient deep learning accelerator. The experimental results demonstrate that our optimized model successfully performs object detection, and the proposed hardware design achieves 1.25× and 4.27× smaller logic and BRAM size, respectively, and its energy consumption is approximately 10.37× lower than previous similar works with 43.95 fps as a real-time process under an operating frequency of 100 MHz on a Xilinx ZC702 FPGA.

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