[go: up one dir, main page]

Ghosh et al., 2023 - Google Patents

Energy-efficient approximate edge inference systems

Ghosh et al., 2023

View PDF
Document ID
4881742521381895094
Author
Ghosh S
Raha A
Raghunathan V
Publication year
Publication venue
ACM Transactions on Embedded Computing Systems

External Links

Snippet

The rapid proliferation of the Internet of Things and the dramatic resurgence of artificial intelligence based application workloads have led to immense interest in performing inference on energy-constrained edge devices. Approximate computing (a design paradigm …
Continue reading at dl.acm.org (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for programme control, e.g. control unit
    • G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30861—Retrieval from the Internet, e.g. browsers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/07—Error detection; Error correction; Monitoring responding to the occurence of a fault, e.g. fault tolerance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F1/00—Details of data-processing equipment not covered by groups G06F3/00 - G06F13/00, e.g. cooling, packaging or power supply specially adapted for computer application
    • G06F1/26—Power supply means, e.g. regulation thereof
    • G06F1/32—Means for saving power
    • G06F1/3203—Power Management, i.e. event-based initiation of power-saving mode
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F1/00—Details of data-processing equipment not covered by groups G06F3/00 - G06F13/00, e.g. cooling, packaging or power supply specially adapted for computer application
    • G06F1/16—Constructional details or arrangements
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce, e.g. shopping or e-commerce

Similar Documents

Publication Publication Date Title
Stanley-Marbell et al. Exploiting errors for efficiency: A survey from circuits to applications
US12347179B2 (en) Privacy-preserving distributed visual data processing
US10963292B2 (en) Techniques to manage virtual classes for statistical tests
US12166688B2 (en) Methods, systems, articles of manufacture and apparatus to optimize resources in edge networks
US11392829B1 (en) Managing data sparsity for neural networks
US20210097449A1 (en) Memory-efficient system for decision tree machine learning
Chakraborty et al. Constructing energy-efficient mixed-precision neural networks through principal component analysis for edge intelligence
Abdel Magid et al. Image classification on IoT edge devices: profiling and modeling
Ghosh et al. Energy-efficient approximate edge inference systems
Daghero et al. Human activity recognition on microcontrollers with quantized and adaptive deep neural networks
Soltaniyeh et al. An accelerator for sparse convolutional neural networks leveraging systolic general matrix-matrix multiplication
CN117980923A (en) Calibration decoder for implementing quantum codes
Li et al. An intelligent collaborative inference approach of service partitioning and task offloading for deep learning based service in mobile edge computing networks
Verhelst et al. Machine learning at the edge
Ruan et al. Community discovery: Simple and scalable approaches
Pasricha et al. Data analytics enables energy-efficiency and robustness: from mobile to manycores, datacenters, and networks (special session paper)
Paul et al. Interactive scheduling for mobile multimedia service in M2M environment
Zhao et al. CE-NAS: An end-to-end carbon-efficient neural architecture search framework
Venieris et al. Nawq-sr: A hybrid-precision npu engine for efficient on-device super-resolution
Zhang et al. Optimization Methods, Challenges, and Opportunities for Edge Inference: A Comprehensive Survey
Leon-Vega et al. Automatic generation of resource and accuracy configurable processing elements
Andreopoulos Error tolerant multimedia stream processing: There's plenty of room at the top (of the system stack)
Okafor et al. Fusing in-storage and near-storage acceleration of convolutional neural networks
Joshua et al. Cross-Platform Optimization of ONNX Models for Mobile and Edge Deployment
Badawy et al. Optimizing thin client caches for mobile cloud computing: Design space exploration using genetic algorithms