IEEE Micro, a bimonthly publication of the IEEE Computer Society, reaches an international audience of microcomputer and microprocessor designers, system integrators, and users. Readers want to increase their technical knowledge and learn the latest industry trends.

Scope

IEEE Micro addresses users and designers of microprocessors and microprocessor systems, including managers, engineers, consultants, educators, and students involved with computers and peripherals, components and subassemblies, communications, instrumentation and control equipment, and guidance systems. Contributions should relate to the design, performance, or application of microprocessors and microcomputers. Tutorials, review papers, and discussions are also welcome. Sample topic areas include architecture, communications, data acquisition, control, hardware and software design/implementation, algorithms (including program listings), digital signal processing, microprocessor support hardware, operating systems, computer aided design, languages, application software, and development systems.

Special Issue on Data-Centric Computing
提出日: 2025-02-27

With the proliferation of mobile and edge computing devices, data generation continues to grow at an exponential rate, reaching an estimated 181 zettabytes processed per year by 2025. In response, computing systems large and small need to process ever-increasing amounts of data quickly and efficiently, leading to the rise of data-centric computing. Data-centric computing covers a broad range of hardware and software co-design topics, spanning techniques that (1) reduce the amount data transmitted, (2) optimize data movement using knowledge of latency and bandwidth of the connections between compute and sources of data, (3) integrate specialized heterogeneous or non-von-Neumann components in data-processing systems, or (4) develop new methods to synthesize or summarize data in place or minimize the overhead of data accesses. A common thread emerging across data-centric computing techniques is the need for hardware/software co-design in compute, memory, storage, and interconnect to deliver sizable improvements in performance and energy efficiency that rely on both traditional and unconventional scaling techniques

This special issue of IEEE Micro solicits academic and industrial research on co-designed solutions that revisit traditional boundaries between compute, memory, storage, interconnect and the software to support new architectures and programming abstractions. The solutions that will meet the test of time will balance specificity with generality, classify general principles, and denote metrics to measure a solution’s benefits and highlight remaining challenges. These solutions will serve as a template for how to apply future innovations in hardware and software to emerging use cases requiring even more generated data.

TOPICS OF INTEREST

    Novel systems that address application domains currently limited by bandwidth or media latency (e.g., large-scale AI training and inference, databases, computational genomics, HPC), and demonstrate dramatic improvements to end-to-end application performance and/or reduction in overall in energy use
    Computation near or in media (e.g., processing-in-memory, processing-near-memory, processing-using-memory, in-storage computing) using digital or analog computational devices and the end-to-end hardware/software infrastructure required to prepare the data for computation
    Techniques to monitor lifetime of data and ensure long-term data resilience of retained data in data-centric computing solutions
    Operational datacenter challenges of migrating existing data and applications to use new data-centric computing solutions to meet future application requirements
    Techniques to mitigate the overhead of multi-tenant data-intensive applications and data processing infrastructure
    Primitives or systems/hardware architectural enhancements using data processing unit/infrastructure processing unit (DPU/IPU) or peer-to-peer data movement for enabling application software to schedule selective parts of large data sets for optimal data movement for when compute becomes available
    Tools to characterize and synthesize data-intensive workloads to model and explore possible system architectures and find new opportunities for efficient data process in compute, interconnects, storage media, and software

Special Issue on Contemporary Industry Products
提出日: 2025-03-13

Topics of Interest

Paper topics are not limited to hardware tapeouts. Papers that are software-centric papers relevant to the computer architecture audience are welcome in this track (e.g. datacenter software work, compiler work, accelerator software stack work), but they should adhere to the tenet that they must be industry papers about production-level work – whether retrospective, planned and on the roadmap, or planned but canceled.

    Processors, SoCs, GPUs, and domain-specific accelerators
    Systems and interconnect technologies for HPC, cloud, or data centers
    Embedded, mobile, and IoT processors
    FPGA or reconfigurable architectures
    Storage and emerging memory systems
    Architectures using emerging technology.
    Architectures for emerging applications including generative AI and bioinformatics.
    Architectures for commercialization of quantum computing

Special Issue on AI for Hardware and Hardware for AI
提出日: 2025-04-25

For years, the computational landscape, stretching from data centers and supercomputers to simple home devices, has predominantly depended on general-purpose processors which were sustainable while Moore’s law guaranteed that chip transistor counts would double approximately every two years. Today, however, as the pace of Moore’s law decelerates, we have witnessed an increasing shift toward hardware accelerators, designed to efficiently utilize hardware resources by concentrating solely on implementing the specific demands of target applications. Hardware accelerators, primarily engineered for an array of AI applications, from computer vision to recommendation systems and natural language processing, have been gaining growing traction, with substantial industrial investments and increasing scholarly interest. While the shift toward hardware accelerators has proven their capabilities, they face new challenges with major AI growth. AI algorithms are not only scaling in size rapidly but also evolving at an accelerated rate. The scale and diversity in modern AI pose a substantial challenge in the design of hardware accelerators for them. As a result, this IEEE Micro Special Issue seeks articles not only related to the hardware accelerators for the next generation of AI but also to the exploration of how AI itself can facilitate the creation of cost-efficient, fast, and scalable hardware. This issue’s topics of interest include, but are not limited to:

    Scalable hardware accelerators for the next generation of large AI models 
    Deploying new technologies (e.g., in-memory computing, photonics, analog computing) for AI efficiency
    Sparsity-aware optimizations techniques for efficient AI 
    Integration of AI techniques to expedite the hardware/software co-design
    Rethinking the software/hardware stack for heterogeneous AI accelerator systems 
    Interconnection networks and data movement optimizations for the future of AI
    Using AI methods to enhance the reliability of hardware accelerators, design validation, and architecture front-end and backend
    Investigating security and privacy challenges in AI-assisted hardware accelerator design