Approximate Computing Methods for Embedded Machine Learning

Ibrahim, Alì; Osta, Mario; Alameh, Mohamad; Saleh, Mahdi; Chiblé, Hussein; Valle, Maurizio

doi:10.1109/icecs.2018.8617877

Cited by 26 publications

(11 citation statements)

References 23 publications

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“…al. [40] explore the use of approximate computing to realize Deep Learning networks on resource constrained embedded platforms. Unlike our work, in these works approximate computing is targeted towards reducing the computational load.…”

Section: B Approximate Computingmentioning

confidence: 99%

Mez: An Adaptive Messaging System for Latency-Sensitive Multi-Camera Machine Vision at the IoT Edge

et al. 2021

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Mez is a novel publish-subscribe messaging system for latency sensitive multi-camera machine vision applications at the IoT Edge. The unlicensed wireless communication in IoT Edge systems are characterized by large latency variations due to intermittent channel interference. To achieve user specified latency in the presence of wireless channel interference, Mez takes advantage of the ability of machine vision applications to temporarily tolerate lower quality video frames if overall application accuracy is not too adversely affected. Control knobs that involve lossy image transformation techniques that modify the frame size, and thereby the video frame transfer latency, are identified. Mez implements a network latency feedback controller that adapts to channel conditions by dynamically adjusting the video frame quality using the image transformation control knobs, so as to simultaneously satisfy latency and application accuracy requirements. Additionally, Mez uses an application domain specific design of the storage layer to provide low latency operations. Experimental evaluation on an IoT Edge testbed with a pedestrian detection machine vision application indicates that Mez is able to tolerate latency variations of up to 10x with a worst-case reduction of 4.2% of the application accuracy F1 score metric. The performance of Mez is also experimentally evaluated against state-of-the-art low latency NATS messaging system.

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Section: B Approximate Computingmentioning

confidence: 99%

Mez: An Adaptive Messaging System for Latency-Sensitive Multi-Camera Machine Vision at the IoT Edge

et al. 2021

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“…Approximate computing techniques can be applied at algorithmic, architecture and circuit levels [16]. Algorithmic level techniques are divided into two categories: dataoriented and process-oriented.…”

Section: Algorithmic Level Approximate Computingmentioning

confidence: 99%

Algorithmic-Level Approximate Tensorial SVM Using High-Level Synthesis on FPGA

et al. 2021

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Approximate Computing Techniques (ACT) are promising solutions towards the achievement of reduced energy, time latency and hardware size for embedded implementations of machine learning algorithms. In this paper, we present the first FPGA implementation of an approximate tensorial Support Vector Machine (SVM) classifier with algorithmic level ACTs using High-Level Synthesis (HLS). A touch modality classification framework was adopted to validate the effectiveness of the proposed implementation. When compared to exact implementation presented in the state-of-the-art, the proposed implementation achieves a reduction in power consumption by up to 49% with a speedup of 3.2×. Moreover, the hardware resources are reduced by 40% while consuming 82% less energy in classifying an input touch with an accuracy loss less than 5%.

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“…Approximate computing (AC) is a promising approach in resource constrained systems, enabling energy efficiency [6]. This has been widely adopted in many areas such as signal processing [7], robotics [8], and machine learning [9]. Reduced precision (RP) computation, a common AC technique, represents the arithmetic data with less bits throughout the computational stack [10] to reduce resources, specifically, memory and logic units, and hence reduces the energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Energy Efficient Approximate 3D Image Reconstruction

Aßmann

Stewart

et al. 2022

IEEE Trans. Emerg. Topics Comput.

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We demonstrate an efficient and accelerated parallel, sparse depth reconstruction framework using compressed sensing (CS) and approximate computing. Employing data parallelism for rapid image formation, the depth image is reconstructed from sparsely sampled scenes using convex optimization. Coupled with faster imaging, this sparse sampling reduces significantly the projected laser power in active systems such as LiDAR to allow eye safe operation at longer range. We also demonstrate how reduced precision is leveraged to reduce the number of logic units in FPGA implementations for such sparse imaging systems. It enables significant reduction in logic units, memory requirements and power consumption by over 80% with minimal impact on the quality of reconstruction. To further accelerate processing, pre-computed, important components of the lower-upper (LU) decomposition and other linear algebraic computations are used to solve the convex optimization problems. Our methodology is demonstrated by the application of the alternating direction method of multipliers (ADMM) and proximal gradient descent (PGD) algorithms. For comparison, a fully discrete least square reconstruction method (dSparse) is also presented. This demonstrates the feasibility of novel, high resolution, low power and high frame rate LiDAR depth imagers based on sparse illumination for use in applications where resources are strictly limited.

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Approximate Computing Methods for Embedded Machine Learning

Cited by 26 publications

References 23 publications

Mez: An Adaptive Messaging System for Latency-Sensitive Multi-Camera Machine Vision at the IoT Edge

Mez: An Adaptive Messaging System for Latency-Sensitive Multi-Camera Machine Vision at the IoT Edge

Algorithmic-Level Approximate Tensorial SVM Using High-Level Synthesis on FPGA

Energy Efficient Approximate 3D Image Reconstruction

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