A Layered Framework for Energy-Efficient Edge Computing in
Edge computing is a concept where data is processed near the originating point and it has the potential to minimize wastes and energy use. This paper is a survey of the existing potential in energy-efficient
Comprehensive Review of Edge Computing for Power
The increasing complexity of conventional energy distribution systems, combined with the growing demand for efficient data processing, has
Solution Brief Elastic and Energy Proportional Edge Computing
The main objective and novelty of the design is to treat energy as a global and elastic resource that can be used smartly by moving compute and data to energy-efficient edge locations.
Towards Energy-Efficient Heterogeneous Multicore Architectures for
This paper deals with the aforementioned issue by exploring typical low power architectures for edge computing.
Energy-Efficient Resource Management in Microservices-Based Fog
This survey offers a comprehensive analysis of existing strategies in microservices-based fog and edge computing environments, with a particular focus on energy-efficient solutions that
Requirements for Energy Efficient Edge Computing: A Survey
In this study, a comprehensive study of the energy efficient Edge Computing has been carried out. There are a lot of research published from the different phases and aspects to reduce energy consumption
Towards Energy-Efficient Heterogeneous Multicore Architectures for
Unfortunately, no standard heterogeneous microcontroller-based architecture exists for edge computing. This paper deals with the aforementioned issue by exploring typical low power architectures for edge
ESAM: ("E")̱nergy-efficient ("S")̱NN ("A")̱rchitecture using 3nm FinFET
The digital implementation of such SNNs offers advantages including enhanced energy efficiency, parallel processing capabilities, and compatibility for hardware acceleration, rendering
Energy-Efficient Edge Intelligence: A Comparative Analysis of AIoT
An additional limitation for IoT devices is the need for energy efficiency, primarily for battery-powered devices. In this paper, we tested the performance, that is, the usability and
Enhancing energy efficiency and cpu utilization in edge computing
The proposed method proposes a novel approach that helps the edge computing technology to overcome the issues of high CPU utilization and low energy efficiency, particularly
Energy-Efficient Edge Intelligence: A Comparative Analysis of AIoT
The findings suggest that accelerators equipped with AI capabilities are also capable of performing autonomous activities at the edge, even on battery-powered devices. A critical scaling
Towards Energy-Efficient and Secure Edge AI: A Cross-Layer
Towards Energy-Efficient and Secure Edge AI: A Cross-Layer Framework To appear at the 40th IEEE/ACM International Conference on Computer-Aided Design (ICCAD), November 2021, Virtual
Energy-Efficient AI on the Edge | Springer Nature Link
This chapter shows methods for the resource-optimized design of AI functionality for edge devices powered by microprocessors or microcontrollers. The goal is to identify Pareto-optimal
Energy-Efficient Edge Optimization Embedded System
Thus, this paper presents an energy-efficient enabled edge optimization embedded system using graph theory for increasing performance in
A comprehensive survey of energy-efficient computing to enable
As such, energy-efficient computing, or "green computing," has become a focal point for researchers seeking to deploy large-scale IoT networks. This study provides a comprehensive
Comprehensive Review of Edge Computing for Power
This system, utilizing an edge computing approach, minimizes cloud dependency by performing energy management closer to the user while also
Energy-efficient AI at the Edge
The advances in artificial intelligence, especially convolutional neural networks (CNNs), over the past few years resulted in state-of-the-art solutions for many tasks, e.g. computer vision. As
An energy-efficient single-cycle RV32I microprocessor for edge
Hence, edge computing systems require low-power solutions while still maintaining a minimum performance level. In this work, an energy-efficient single-cycle RISC-V instruction set
Tunable Energy-Efficient Approximate Circuits for Self-Powered AI
Therefore, we propose novel approximate compressors to design multiply and accumulate (MAC) hardware unit of Deep Neural Network and Convolutional Neural Network (DNN/CNN) that achieve
Energy Efficiency on Edge Computing: Challenges and Vision
We further discuss different approaches such as computation offloading, edge devices hardware and software designs, and a number of algorithms that help reduce energy consumption. Finally, we
SparkNoC: An energy-efficiency FPGA-based accelerator
SparkNoC: An energy-efficiency FPGA-based accelerator using optimized lightweight CNN for edge computing Ming Xia a b, Zunkai Huang a, Li Tian a, Hui Wang a, Victor Chang c,
Energy-Efficient Neuromorphic Computing for Edge AI: A
This paper presented NeuEdge, a comprehensive neuromorphic computing framework enabling energy-efficient edge AI through integrated optimization of spike encoding, network design,
(PDF) Edge Computing in IoT Networks: Enhancing
This work concludes that edge computing is a major breakthrough in iot networks and an enabling technology for real-time, efficient and sustainable
Power Estimation Models for Edge Computing Devices
Our methodology can be applied to any edge de- vice, providing insights into the most efficient power consumption model. The heterogeneity of edge devices poses a challenge to creating a global power
Energy-efficient offloading framework for mobile edge/cloud computing
Energy efficiency is one of the most critical aspects of modern computing paradigms due to minimizing carbon footprint and lowering operational costs. To achieve efficiency, the typical
Energy-Efficient FPGA Design for IoT and Edge Computing
This research underscores the role of energy-efficient FPGA design in enabling scalable, low-power IoT and edge computing applications.
Optimizing Energy Efficient Cloud Architectures for Edge Computing:
Moreover, lowering cloud-edge systems'' energy footprints is essential for fostering sustainability in light of growing concerns about environmental effects. This research presents a comprehensive review of
Edge AI for Energy-Efficient Computing: A Systematic Review of
Background and Purpose: Edge Artificial Intelligence (AI) has emerged as a crucial solution for minimizing power consumption during real-time data processing in computing devices.
Energy-efficient deep learning inference on edge devices
In contrast, executing these models on cost and power-constrained edge devices requires a synergy of hardware specialization (i.e., accelerators) and model optimization. In this chapter, we describe
Sustainable Energy-Efficient Multi-Objective Task Processing Based
As smart cities evolve, rising computational demands strain infrastructures. Offloading tasks to edge cloud data centers offers potential but faces challenges like high latency, energy use, and data