The Braking-Pressure and Driving-Direction Determination System (BDDS) Using Road Roughness and Passenger Conditions of Surrounding Vehicles

Jeong, Youngjin; Son, SuRak; Lee, Byung-kwan; Lee, Su-Hee

doi:10.3390/s22124414

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…Deep learning has been popular because of the availability of computing power and the development of big data [ 1 ], and various reviews and discussions on deep learning have been extensively conducted in recent years [ 2 , 3 , 4 , 5 , 6 ]. It has been widely applied in many fields such as image recognition [ 7 ], object detection [ 8 ], autonomous driving [ 9 , 10 ], and robotics [ 11 ]. Moreover, deep learning networks have been shown to be successful for these fields [ 12 ], and nowadays it has become important even in the field of IoT with the rapid development of IoT devices and network infrastructure [ 13 ].…”

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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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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The Efficiency of Convolution on Gemmini Deep Learning Hardware Accelerator

Gookyi,

Wilson,

Ahiadormey

et al. 2023

2023 Ieee Africon

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Deep Learning Accelerators’ Configuration Space Exploration Effect on Performance and Resource Utilization: A Gemmini Case Study

Gookyi

Lee

Jang

et al. 2023

Sensors

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Though custom deep learning (DL) hardware accelerators are attractive for making inferences in edge computing devices, their design and implementation remain a challenge. Open-source frameworks exist for exploring DL hardware accelerators. Gemmini is an open-source systolic array generator for agile DL accelerator exploration. This paper details the hardware/software components generated using Gemmini. The general matrix-to-matrix multiplication (GEMM) of different dataflow options, including output/weight stationary (OS/WS), was explored in Gemmini to estimate the performance relative to a CPU implementation. The Gemmini hardware was implemented on an FPGA device to explore the effect of several accelerator parameters, including array size, memory capacity, and the CPU/hardware image-to-column (im2col) module, on metrics such as the area, frequency, and power. This work revealed that regarding the performance, the WS dataflow offered a speedup of 3× relative to the OS dataflow, and the hardware im2col operation offered a speedup of 1.1× relative to the operation on the CPU. For hardware resources, an increase in the array size by a factor of 2 led to an increase in both the area and power by a factor of 3.3, and the im2col module led to an increase in area and power by factors of 1.01 and 1.06, respectively.

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The Braking-Pressure and Driving-Direction Determination System (BDDS) Using Road Roughness and Passenger Conditions of Surrounding Vehicles

Cited by 3 publications

References 32 publications

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

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

The Efficiency of Convolution on Gemmini Deep Learning Hardware Accelerator

Deep Learning Accelerators’ Configuration Space Exploration Effect on Performance and Resource Utilization: A Gemmini Case Study

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