Titlebook: Approximate Computing; Weiqiang Liu,Fabrizio Lombardi Book 2022 The Editor(s) (if applicable) and The Author(s), under exclusive license t

Robust

Forehead-Lift

Approximate Computing for Machine Learning Workloads: A Circuits and Systems Perspectivemitives such as multipliers, adders, memories, and matrix vector multiplication units which are vital to build a machine learning accelerator. Several algorithm techniques that can utilize the aforementioned hardware level approximations to accelerate machine learning workloads are also discussed.

NOCT

Book 2022 in a single-source the state-of-the-art, covering the entire spectrum of research activities in approximate computing, bridging device, circuit, architecture, and system levels.  Content includes tutorials, reviews and surveys of current theoretical/experimental results, design methodologies and ap

stress-response

https://doi.org/10.1007/978-3-322-82395-3ing devices to securing machine learning models in the main memory. Our goal is to demonstrate the security promises and pitfalls of VOS to the engineers building the next generation of low-power voltage-overscaled systems.

刀锋

Voltage Overscaling Techniques for Security Applicationsing devices to securing machine learning models in the main memory. Our goal is to demonstrate the security promises and pitfalls of VOS to the engineers building the next generation of low-power voltage-overscaled systems.

Missile

Intractable

An Automated Logic-Level Framework for Approximate Modular Arithmetic Circuitsimations under a given error constraint to design approximate arithmetic circuits. Our proposed methodology improves on existing state-of-the-art manual design techniques and is evaluated by using them in error-tolerant case studies such as image sharpening and canny edge detection.

nocturnal

Approximate Multiplier Design for Energy Efficiency: From Circuit to Algorithmincurs design complexities and challenges in selecting the appropriate multiplier for a particular application. Thus, this paper provides a comprehensive review of the state-of-the-art (SOTA) designs of approximate multipliers for future investigations.

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钻孔

Majority Logic-Based Approximate Multipliers for Error-Tolerant Applicationspproximate multiplier designs are comparatively evaluated from error and circuit characteristics. Image processing and neural networks as case studies of error-tolerant applications are presented to show the validity and advantages of the proposed designs.