Embedded Operating System Optimization through Floating to Fixed Point Compiler Transformation

Cattaneo, Daniele; Bello, Antonio Di; Cherubin, Stefano; Terraneo, Federico; Agosta, Giovanni

doi:10.1109/dsd.2018.00042

Cited by 14 publications

(11 citation statements)

References 13 publications

(10 reference statements)

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“…Taking the Hi3559 series of chips as an example, the inner NNIE core calculates the fixed-point data much faster than the floating-point numbers. Therefore, the data need to complete the transformation from floating-point number to fixed-point number before entering the computer core [ 16 ].…”

Section: Core Acceleration Methodsmentioning

confidence: 99%

Real-Time Detection of Cook Assistant Overalls Based on Embedded Reasoning

Sheng¹,

Sheng²,

Gao³

et al. 2021

Sensors

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Currently, the target detection based on convolutional neural network plays an important role in image recognition, speech recognition and other fields. However, the current network model features a complex structure, a huge number of parameters and resources. These conditions make it difficult to apply in embedded devices with limited computational capabilities and extreme sensitivity to power consumption. In this regard, the application scenarios of deep learning are limited. This paper proposes a real-time detection scheme for cook assistant overalls based on the Hi3559A embedded processor. With YOLOv3 as the benchmark network, this scheme fully mobilizes the hardware acceleration resources through the network model optimization and the parallel processing technology of the processor, and improves the network reasoning speed, so that the embedded device can complete the task of real-time detection on the local device. The experimental results show that through the purposeful cropping, segmentation and in-depth optimization of the neural network according to the specific processor, the neural network can recognize the image accurately. In an application environment where the power consumption is only 5.5 W, the recognition speed of the neural network on the embedded end is increased to about 28 frames (the design requirement was to achieve a recognition speed of 25 frames or more), so that the optimized network can be effectively applied in the back kitchen overalls identification scene.

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Section: Core Acceleration Methodsmentioning

confidence: 99%

Real-Time Detection of Cook Assistant Overalls Based on Embedded Reasoning

Sheng¹,

Sheng²,

Gao³

et al. 2021

Sensors

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“…Proposals have been made at the HW level to take advantage of inherent perceptual limitations, redundant data, or reduced precision input [15], as well as to reduce system costs or improve power efficiency [16]. At the same time, works on floating-point to fixed-point conversion tools [17], [18] allow us to trade-off the algorithm exactness for a more efficient implementation. Finally, new data types are emerging including BFloat16 [19] and Posit [20].…”

Section: Programming Models and Toolchainsmentioning

confidence: 99%

TEXTAROSSA: Towards EXtreme scale Technologies and Accelerators for euROhpc hw/Sw Supercomputing Applications for exascale

Agosta

Cattaneo

Fornaciari

et al. 2021

2021 24th Euromicro Conference on Digital System Design (DSD)

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“…Due to the structure of the input dataset, it is not possible to meaningfully differentiate the input classes through requirements (1) through (4). More in detail, requirements (1) and (4) are always satisfied, as every single triangle intersection test is independent and of a predetermined constant size.…”

Section: Jmeintmentioning

confidence: 99%

“…taffo is a precision tuning assistant that has already been employed in the embedded systems [4] and in the approximate computing domain [8] to convert floating point code into fixed point. More specifically, the taffo toolchain supplies the code analysis, the code manipulation, and the code validation software components.…”

mentioning

confidence: 99%

Dynamic Precision Autotuning with TAFFO

Cherubin

Cattaneo

Chiari

et al. 2020

ACM Trans. Archit. Code Optim.

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Many classes of applications, both in the embedded and high performance domains, can trade off the accuracy of the computed results for computation performance. One way to achieve such a trade-off is precision tuning-that is, to modify the data types used for the computation by reducing the bit width, or by changing the representation from floating point to fixed point. We present a methodology for high-accuracy dynamic precision tuning based on the identification of input classes (i.e., classes of input datasets that benefit from similar optimizations). When a new input region is detected, the application kernels are re-compiled on the fly with the appropriate selection of parameters. In this way, we obtain a continuous optimization approach that enables the exploitation of the reduced precision computation while progressively exploring the solution space, thus reducing the time required by compilation overheads. We provide tools to support the automation of the runtime part of the solution, leaving to the user only the task of identifying the input classes. Our approach provides a significant performance boost (up to 320%) on the typical approximate computing benchmarks, without meaningfully affecting the accuracy of the result, since the error remains always below 3%.

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Embedded Operating System Optimization through Floating to Fixed Point Compiler Transformation

Cited by 14 publications

References 13 publications

Real-Time Detection of Cook Assistant Overalls Based on Embedded Reasoning

Real-Time Detection of Cook Assistant Overalls Based on Embedded Reasoning

TEXTAROSSA: Towards EXtreme scale Technologies and Accelerators for euROhpc hw/Sw Supercomputing Applications for exascale

Dynamic Precision Autotuning with TAFFO

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