Loading [MathJax]/extensions/MathZoom.js
Bashir M. Al-Hashimi - IEEE Xplore Author Profile
Subscribe
ADVANCED SEARCH

Author details

< Back

Bashir M. Al-Hashimi

Also published under: B. M. Al-Hashimi, B. Al-Hashimi, Bashir Al-Hashimi, Bashir M. Al-hashimi, Bashir M. Al Hashimi, B. M. Al Hashimi

Publications
243
Citations
4,003
Publications by Year
19882025
Co-Authors:
Show All Co-Authors (217)
Image of author Bashir M. Al-Hashimi

Bashir M. Al-Hashimi

Also published under: B. M. Al-Hashimi, B. Al-Hashimi, Bashir Al-Hashimi, Bashir M. Al-hashimi, Bashir M. Al Hashimi, B. M. Al Hashimi

Affiliation

Department of Engineering
Centre for Intelligent Information Processing Systems (CIIPS)
King’s College London, London, U.K.

Publication Topics

Biography

Bashir M. Al-Hashimi is an ARM Professor of computer engineering, the Dean of engineering and physical sciences, University of Southampton, Southampton, U.K. His research interests include methods, algorithms, and design automation tools for low-power design and test of embedded-computing systems. He is a Fellow of the IEEE and the U.K. Royal Academy of Engineering.(Based on document published on 23 March 2020).
Publications
243
Citations
4,003
Publications by Year
19882025
Co-Authors:
Show All Co-Authors (217)
Author's Published Works

Showing 1-25 of 242 results

  • Sort

Filter Results

Show

Sort By

Results

Bayes2IMC: In-Memory Computing for Bayesian Binary Neural Networks

Prabodh Katti;Clement Ruah;Osvaldo Simeone;Bashir M. Al-Hashimi;Bipin Rajendran

IEEE Transactions on Circuits and Systems I: Regular Papers
Year: 2025 | Early Access Article |
Bayesian Neural Networks (BNNs) generate an ensemble of possible models by treating model weights as random variables. This enables them to provide superior estimates of decision uncertainty. However, implementing Bayesian inference in hardware is resource-intensive, as it requires noise sources to generate the desired model weights. In this work, we introduce Bayes2IMC, an in-memory computing (IM...Show More

Bayes2IMC: In-Memory Computing for Bayesian Binary Neural Networks

Prabodh Katti;Clement Ruah;Osvaldo Simeone;Bashir M. Al-Hashimi;Bipin Rajendran

Year: 2025 | Early Access Article |

Hardware-Software Co-optimised Fast and Accurate Deep Reconfigurable Spiking Inference Accelerator Architecture Design Methodology

Anagha Nimbekar;Prabodh Katti;Chen Li;Bashir M. Al-Hashimi;Amit Acharyya;Bipin Rajendran

2024 IEEE 37th International System-on-Chip Conference (SOCC)
Year: 2024 | Conference Paper |
Spiking Neural Networks (SNNs) have emerged as a promising approach to improve the energy efficiency of machine learning models, as they naturally implement event-driven computations while avoiding expensive multiplication operations.In this paper, we develop a hardware-software co-optimisation strategy to port software-trained deep neural networks (DNN) to reduced-precision spiking models demonst...Show More

Hardware-Software Co-optimised Fast and Accurate Deep Reconfigurable Spiking Inference Accelerator Architecture Design Methodology

Anagha Nimbekar;Prabodh Katti;Chen Li;Bashir M. Al-Hashimi;Amit Acharyya;Bipin Rajendran

Year: 2024 | Conference Paper |

Digital Twin-Based Multiple Access Optimization and Monitoring via Model-Driven Bayesian Learning

Clement Ruah;Osvaldo Simeone;Bashir Al-Hashimi

ICC 2023 - IEEE International Conference on Communications
Year: 2023 | Conference Paper |
Cited by: Papers (6)
Commonly adopted in the manufacturing and aerospace sectors, digital twin (DT) platforms are increasingly seen as a promising paradigm to control and monitor software-based, “open”, communication systems, which play the role of the physical twin (PT). In the general framework presented in this work, the DT builds a Bayesian model of the communication system, which is leveraged to enable core DT fu...Show More

Digital Twin-Based Multiple Access Optimization and Monitoring via Model-Driven Bayesian Learning

Clement Ruah;Osvaldo Simeone;Bashir Al-Hashimi

Year: 2023 | Conference Paper |

A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems

Clement Ruah;Osvaldo Simeone;Bashir M. Al-Hashimi

IEEE Journal on Selected Areas in Communications
Year: 2023 | Volume: 41, Issue: 10 | Journal Article |
Cited by: Papers (16)
Commonly adopted in the manufacturing and aerospace sectors, digital twin (DT) platforms are increasingly seen as a promising paradigm to control, monitor, and analyze software-based, “open”, communication systems that are expected to dominate 6G deployments. Notably, DT platforms provide a sandbox in which to test artificial intelligence (AI) solutions for communication systems, potentially reduc...Show More

A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems

Clement Ruah;Osvaldo Simeone;Bashir M. Al-Hashimi

Year: 2023 | Volume: 41, Issue: 10 | Journal Article |

Bayesian Inference on Binary Spiking Networks Leveraging Nanoscale Device Stochasticity

Prabodh Katti;Nicolas Skatchkovsky;Osvaldo Simeone;Bipin Rajendran;Bashir M. Al-Hashimi

2023 IEEE International Symposium on Circuits and Systems (ISCAS)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Bayesian Neural Networks (BNNs) can overcome the problem of overconfidence that plagues traditional frequentist deep neural networks, and are hence considered to be a key enabler for reliable AI systems. However, conventional hardware realizations of BNNs are resource intensive, requiring the imple-mentation of random number generators for synaptic sampling. Owing to their inherent stochasticity d...Show More

Bayesian Inference on Binary Spiking Networks Leveraging Nanoscale Device Stochasticity

Prabodh Katti;Nicolas Skatchkovsky;Osvaldo Simeone;Bipin Rajendran;Bashir M. Al-Hashimi

Year: 2023 | Conference Paper |

Content- and Lighting-Aware Adaptive Brightness Scaling for Improved Mobile User Experience

Samuel Isuwa;David Amos;Amit Kumar Singh;Bashir M. Al-Hashimi;Geoff V. Merrett

2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)
Year: 2023 | Conference Paper |
For an improved user experience, the display sub-system is expected to provide superior resolution and optimal brightness despite its impact on battery life. Existing brightness scaling approaches set the display brightness statically or adaptively in response to predefined events such as low-battery or ambient light of the environment, which are independent of the displayed content. Approaches th...Show More

Content- and Lighting-Aware Adaptive Brightness Scaling for Improved Mobile User Experience

Samuel Isuwa;David Amos;Amit Kumar Singh;Bashir M. Al-Hashimi;Geoff V. Merrett

Year: 2023 | Conference Paper |

Power-Efficient and Aging-Aware Primary/Backup Technique for Heterogeneous Embedded Systems

Mohsen Ansari;Sepideh Safari;Nezam Rohbani;Alireza Ejlali;Bashir M. Al-Hashimi

IEEE Transactions on Sustainable Computing
Year: 2023 | Volume: 8, Issue: 4 | Journal Article |
Cited by: Papers (3)
One of the essential requirements of embedded systems is a guaranteed level of reliability. In this regard, fault-tolerance techniques are broadly applied to these systems to enhance reliability. However, fault-tolerance techniques may increase power consumption due to their inherent redundancy. For this purpose, power management techniques are applied, along with fault-tolerance techniques, which...Show More

Power-Efficient and Aging-Aware Primary/Backup Technique for Heterogeneous Embedded Systems

Mohsen Ansari;Sepideh Safari;Nezam Rohbani;Alireza Ejlali;Bashir M. Al-Hashimi

Year: 2023 | Volume: 8, Issue: 4 | Journal Article |

Similarity-Aware CNN for Efficient Video Recognition at the Edge

Amin Sabet;Jonathon Hare;Bashir M. Al-Hashimi;Geoff V. Merrett

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Year: 2022 | Volume: 41, Issue: 11 | Journal Article |
Cited by: Papers (2)
Convolutional neural networks (CNNs) often extract similar features from successive video frames due to having identical appearances. In contrast, conventional CNNs for video recognition process individual frames with a fixed computational effort. Each video frame is independently processed, resulting in numerous redundant computations and an inefficient use of limited energy resources, particular...Show More

Similarity-Aware CNN for Efficient Video Recognition at the Edge

Amin Sabet;Jonathon Hare;Bashir M. Al-Hashimi;Geoff V. Merrett

Year: 2022 | Volume: 41, Issue: 11 | Journal Article |

Mitigating Interactive Performance Degradation From Mobile Device Thermal Throttling

James R. B. Bantock;Bashir M. Al-Hashimi;Geoff V. Merrett

IEEE Embedded Systems Letters
Year: 2021 | Volume: 13, Issue: 1 | Journal Article |
Cited by: Papers (4)
Mobile devices are limited in mass and volume, reducing the viability of active device cooling implementations. This requires the use of less effective passive techniques to maintain device skin temperature levels. Application performance demands on a modern mobile device are driven by sustained performance workloads, such as 3-D games, virtual, and augmented reality. Mobile system-on-chips (SoCs)...Show More

Mitigating Interactive Performance Degradation From Mobile Device Thermal Throttling

James R. B. Bantock;Bashir M. Al-Hashimi;Geoff V. Merrett

Year: 2021 | Volume: 13, Issue: 1 | Journal Article |

Optimising Resource Management for Embedded Machine Learning

Lei Xun;Long Tran-Thanh;Bashir M Al-Hashimi;Geoff V. Merrett

2020 Design, Automation & Test in Europe Conference & Exhibition (DATE)
Year: 2020 | Conference Paper |
Cited by: Papers (10)
Machine learning inference is increasingly being executed locally on mobile and embedded platforms, due to the clear advantages in latency, privacy and connectivity. In this paper, we present approaches for online resource management in heterogeneous multi-core systems and show how they can be applied to optimise the performance of machine learning work-loads. Performance can be defined using plat...Show More

Optimising Resource Management for Embedded Machine Learning

Lei Xun;Long Tran-Thanh;Bashir M Al-Hashimi;Geoff V. Merrett

Year: 2020 | Conference Paper |

Run-Time Protection of Multi-Core Processors From Power-Noise Denial-of-Service Attacks

Vasileios Tenentes;Shidhartha Das;Daniele Rossi;Bashir M. Al-Hashimi

IEEE Transactions on Device and Materials Reliability
Year: 2020 | Volume: 20, Issue: 2 | Journal Article |
Cited by: Papers (3)
In this paper, we show that stress-tests can be potentially used as power-noise viruses in denial-of-service (DoS) attacks by causing voltage emergencies that may lead to data corruptions and system crashes in multi-core processors. This attack targets processors whose operating voltage has been reduced in-the-field for improving energy efficiency. To protect such undervolted processors from this ...Show More

Run-Time Protection of Multi-Core Processors From Power-Noise Denial-of-Service Attacks

Vasileios Tenentes;Shidhartha Das;Daniele Rossi;Bashir M. Al-Hashimi

Year: 2020 | Volume: 20, Issue: 2 | Journal Article |

Dynamic Energy and Thermal Management of Multi-core Mobile Platforms: A Survey

Amit Kumar Singh;Somdip Dey;Klaus McDonald-Maier;Karunakar Reddy Basireddy;Geoff V. Merrett;Bashir M. Al-Hashimi

IEEE Design & Test
Year: 2020 | Volume: 37, Issue: 5 | Magazine Article |
Cited by: Papers (40)
Editor's notes: Multicore mobile processors have proliferated due to their efficiency in meeting the stringent performance requirement within a limited power budget. However, their continued success depends critically on managing their power and performance in the presence of dynamic workload variations. This article provides a survey of dynamic energy and thermal management approaches for multico...Show More

Dynamic Energy and Thermal Management of Multi-core Mobile Platforms: A Survey

Amit Kumar Singh;Somdip Dey;Klaus McDonald-Maier;Karunakar Reddy Basireddy;Geoff V. Merrett;Bashir M. Al-Hashimi

Year: 2020 | Volume: 37, Issue: 5 | Magazine Article |

Verifying Cross-Layer Interactions Through Formal Model-Based Assertion Generation

Asieh Salehi Fathabadi;Mohammadsadegh Dalvandi;Michael Butler;Bashir M. Al-Hashimi

IEEE Embedded Systems Letters
Year: 2020 | Volume: 12, Issue: 3 | Journal Article |
Cited by: Papers (1)
Cross-layer runtime management (RTM) frameworks for embedded systems provide a set of standard application programming interfaces (APIs) for communication between different system layers (i.e., RTM, applications, and device) and simplify the development process by abstracting these layers. Integration of independently developed components of the system is an error-prone process that requires caref...Show More

Verifying Cross-Layer Interactions Through Formal Model-Based Assertion Generation

Asieh Salehi Fathabadi;Mohammadsadegh Dalvandi;Michael Butler;Bashir M. Al-Hashimi

Year: 2020 | Volume: 12, Issue: 3 | Journal Article |

Collaborative Adaptation for Energy-Efficient Heterogeneous Mobile SoCs

Amit Kumar Singh;Karunakar Reddy Basireddy;Alok Prakash;Geoff V. Merrett;Bashir M. Al-Hashimi

IEEE Transactions on Computers
Year: 2020 | Volume: 69, Issue: 2 | Journal Article |
Cited by: Papers (17)
Heterogeneous Mobile System-on-Chips (SoCs) containing CPU and GPU cores are becoming prevalent in embedded computing, and they need to execute applications concurrently. However, existing run-time management approaches do not perform adaptive mapping and thread-partitioning of applications while exploiting both CPU and GPU cores at the same time. In this paper, we propose an adaptive mapping and ...Show More

Collaborative Adaptation for Energy-Efficient Heterogeneous Mobile SoCs

Amit Kumar Singh;Karunakar Reddy Basireddy;Alok Prakash;Geoff V. Merrett;Bashir M. Al-Hashimi

Year: 2020 | Volume: 69, Issue: 2 | Journal Article |

AdaMD: Adaptive Mapping and DVFS for Energy-Efficient Heterogeneous Multicores

Karunakar R. Basireddy;Amit Kumar Singh;Bashir M. Al-Hashimi;Geoff V. Merrett

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Year: 2020 | Volume: 39, Issue: 10 | Journal Article |
Cited by: Papers (30)
Modern heterogeneous multicore systems, containing various types of cores, are increasingly dealing with concurrent execution of dynamic application workloads. Moreover, the performance constraints of each application vary, and applications enter/exit the system at any time. Existing approaches are not efficient in such dynamic scenarios, especially if applications are unknown, as they require ext...Show More

AdaMD: Adaptive Mapping and DVFS for Energy-Efficient Heterogeneous Multicores

Karunakar R. Basireddy;Amit Kumar Singh;Bashir M. Al-Hashimi;Geoff V. Merrett

Year: 2020 | Volume: 39, Issue: 10 | Journal Article |

Incremental Training and Group Convolution Pruning for Runtime DNN Performance Scaling on Heterogeneous Embedded Platforms

Lei Xun;Long Tran-Thanh;Bashir M Al-Hashimi;Geoff V. Merrett

2019 ACM/IEEE 1st Workshop on Machine Learning for CAD (MLCAD)
Year: 2019 | Conference Paper |
Cited by: Papers (4)
Inference for Deep Neural Networks is increasingly being executed locally on mobile and embedded platforms due to its advantages in latency, privacy and connectivity. Since modern System on Chips typically execute a combination of different and dynamic workloads concurrently, it is challenging to consistently meet inference time/energy budget at runtime because of the local computing resources ava...Show More

Incremental Training and Group Convolution Pruning for Runtime DNN Performance Scaling on Heterogeneous Embedded Platforms

Lei Xun;Long Tran-Thanh;Bashir M Al-Hashimi;Geoff V. Merrett

Year: 2019 | Conference Paper |

Run-time Detection and Mitigation of Power-Noise Viruses

Vasileios Tenentes;Shidhartha Das;Daniele Rossi;Bashir M. Al-Hashimi

2019 IEEE 25th International Symposium on On-Line Testing and Robust System Design (IOLTS)
Year: 2019 | Conference Paper |
Cited by: Papers (1)
Power-noise viruses can be used as denial-of-service attacks by causing voltage emergencies in multi-core microprocessors that may lead to data corruptions and system crashes. In this paper, we present a run-time system for detecting and mitigating power-noise viruses. We present voltage noise data from a power-noise virus and benchmarks collected from an Arm multi-core processor, and we observe t...Show More

Run-time Detection and Mitigation of Power-Noise Viruses

Vasileios Tenentes;Shidhartha Das;Daniele Rossi;Bashir M. Al-Hashimi

Year: 2019 | Conference Paper |

A Sub-nW/kHz Relaxation Oscillator With Ratioed Reference and Sub-Clock Power Gated Comparator

Anand Savanth;Alex S. Weddell;James Myers;David Flynn;Bashir M. Al-Hashimi

IEEE Journal of Solid-State Circuits
Year: 2019 | Volume: 54, Issue: 11 | Journal Article |
Cited by: Papers (22)
Realizing the vision of a trillion IoT sensor nodes demands ultra low-power (ULP) compute, typically implemented using synchronous digital systems. These require low-power clock sources which must be fully integrated to meet low system-cost requirements. Hence, relaxation oscillators (RxOs) are popular. Their need for precision references and high-speed comparators can challenge power budgets; hen...Show More

A Sub-nW/kHz Relaxation Oscillator With Ratioed Reference and Sub-Clock Power Gated Comparator

Anand Savanth;Alex S. Weddell;James Myers;David Flynn;Bashir M. Al-Hashimi

Year: 2019 | Volume: 54, Issue: 11 | Journal Article |

BRB: Mitigating Branch Predictor Side-Channels

Ilias Vougioukas;Nikos Nikoleris;Andreas Sandberg;Stephan Diestelhorst;Bashir M. Al-Hashimi;Geoff V. Merrett

2019 IEEE International Symposium on High Performance Computer Architecture (HPCA)
Year: 2019 | Conference Paper |
Modern processors use branch prediction as an optimization to improve processor performance. Predictors have become larger and increasingly more sophisticated in order to achieve higher accuracies which are needed in high performance cores. However, branch prediction can also be a source of side channel exploits, as one context can deliberately change the branch predictor state and alter the instr...Show More

BRB: Mitigating Branch Predictor Side-Channels

Ilias Vougioukas;Nikos Nikoleris;Andreas Sandberg;Stephan Diestelhorst;Bashir M. Al-Hashimi;Geoff V. Merrett

Year: 2019 | Conference Paper |

Predictive Thermal Management for Energy-Efficient Execution of Concurrent Applications on Heterogeneous Multicores

Eduardo Weber Wächter;Cédric de Bellefroid;Karunakar Reddy Basireddy;Amit Kumar Singh;Bashir M. Al-Hashimi;Geoff Merrett

IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Year: 2019 | Volume: 27, Issue: 6 | Journal Article |
Cited by: Papers (23)
Current multicore platforms contain different types of cores, organized in clusters (e.g., ARM's big.LITTLE). These platforms deal with concurrently executing applications, having varying workload profiles and performance requirements. Runtime management is imperative for adapting to such performance requirements and workload variabilities and to increase energy and temperature efficiency. Tempera...Show More

Predictive Thermal Management for Energy-Efficient Execution of Concurrent Applications on Heterogeneous Multicores

Eduardo Weber Wächter;Cédric de Bellefroid;Karunakar Reddy Basireddy;Amit Kumar Singh;Bashir M. Al-Hashimi;Geoff Merrett

Year: 2019 | Volume: 27, Issue: 6 | Journal Article |

Arbitrarily Parallel Turbo Decoding for Ultra-Reliable Low Latency Communication in 3GPP LTE

Luping Xiang;Matthew F. Brejza;Robert G. Maunder;Bashir M. Al-Hashimi;Lajos Hanzo

IEEE Journal on Selected Areas in Communications
Year: 2019 | Volume: 37, Issue: 4 | Journal Article |
Cited by: Papers (17)
In order to meet the latency requirements of the ultra-reliable low latency communication (URLLC) mode of the third-generation partnership project's long term evolution (LTE) mobile communication standard, this paper proposes a novel turbo decoding algorithm that supports an arbitrarily high degree of parallel processing, facilitating significantly higher processing throughputs and substantially l...Show More

Arbitrarily Parallel Turbo Decoding for Ultra-Reliable Low Latency Communication in 3GPP LTE

Luping Xiang;Matthew F. Brejza;Robert G. Maunder;Bashir M. Al-Hashimi;Lajos Hanzo

Year: 2019 | Volume: 37, Issue: 4 | Journal Article |

Survey of Turbo, LDPC, and Polar Decoder ASIC Implementations

Shuai Shao;Peter Hailes;Tsang-Yi Wang;Jwo-Yuh Wu;Robert G. Maunder;Bashir M. Al-Hashimi;Lajos Hanzo

IEEE Communications Surveys & Tutorials
Year: 2019 | Volume: 21, Issue: 3 | Journal Article |
Cited by: Papers (102)
Channel coding may be viewed as the best-informed and most potent component of cellular communication systems, which is used for correcting the transmission errors inflicted by noise, interference, and fading. The powerful turbo code was selected to provide channel coding for mobile broad band data in the 3G UMTS and 4G long term evolution cellular systems. However, the 3GPP standardization group ...Show More

Survey of Turbo, LDPC, and Polar Decoder ASIC Implementations

Shuai Shao;Peter Hailes;Tsang-Yi Wang;Jwo-Yuh Wu;Robert G. Maunder;Bashir M. Al-Hashimi;Lajos Hanzo

Year: 2019 | Volume: 21, Issue: 3 | Journal Article |

Low-Complexity Architecture for Cyber-Physical Systems Model Identification

Charan Kumar Vala;Mark French;Amit Acharyya;Bashir M. Al-Hashimi

IEEE Transactions on Circuits and Systems II: Express Briefs
Year: 2019 | Volume: 66, Issue: 8 | Journal Article |
Cited by: Papers (4)
We propose a low complexity architecture for cyber-physical system (CPS) model identification based on multiple-model adaptive estimation (MMAE) algorithms. The complexity reduction is achieved by reducing the number of multiplications in the filter banks of the MMAE algorithm present in the cyber component of the CPS. The architecture has been implemented using FPGA for 16, 32, 64 filter banks as...Show More

Low-Complexity Architecture for Cyber-Physical Systems Model Identification

Charan Kumar Vala;Mark French;Amit Acharyya;Bashir M. Al-Hashimi

Year: 2019 | Volume: 66, Issue: 8 | Journal Article |

Graceful Performance Adaption through Hardware-Software Interaction for Autonomous Battery Management of Multicore Smartphones

Anup Das;Domenico Balsamo;Geoff. V. Merrett;Bashir M. Al-Hashimi;Francky Catthoor

2018 Ninth International Green and Sustainable Computing Conference (IGSC)
Year: 2018 | Conference Paper |
Cited by: Papers (2)
Despite advances in multicore smartphone technologies, battery consumption still remains one of customer's least satisfying features. This is because existing energy saving techniques do not consider the electrochemical characteristics of batteries, which causes battery consumption to vary unpredictably, both within and across applications. Additionally, these techniques provide application specif...Show More

Graceful Performance Adaption through Hardware-Software Interaction for Autonomous Battery Management of Multicore Smartphones

Anup Das;Domenico Balsamo;Geoff. V. Merrett;Bashir M. Al-Hashimi;Francky Catthoor

Year: 2018 | Conference Paper |

Workload-Aware Runtime Energy Management for HPC Systems

Karunakar Reddy Basireddy;Eduardo Weber Wachter;Bashir M. Al-Hashimi;Geoff Merrett

2018 International Conference on High Performance Computing & Simulation (HPCS)
Year: 2018 | Conference Paper |
Cited by: Papers (9)
Energy efficiency has become a crucial factor in high-performance computing, mainly due to its effect on operating costs and failure rates of computing platforms. To improve the energy efficiency of such systems, processors are equipped with low-power techniques such as dynamic voltage and frequency scaling (DVFS) and power capping. These techniques have to be controlled carefully as per the workl...Show More

Workload-Aware Runtime Energy Management for HPC Systems

Karunakar Reddy Basireddy;Eduardo Weber Wachter;Bashir M. Al-Hashimi;Geoff Merrett

Year: 2018 | Conference Paper |

IEEE Account

Purchase Details

Profile Information

Need Help?

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.