Journal Information
ACM Journal on Emerging Technologies in Computing Systems (JETC)
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Call For Papers
 The ACM Journal on Emerging Technologies in Computing Systems invites submissions of original technical papers describing research and development in emerging technologies in computing systems. Major economic and technical challenges are expected to impede the continued scaling of semiconductor devices. This has resulted in the search for alternate mechanical, biological/biochemical, nanoscale electronic, asynchronous and quantum computing, and sensor technologies. As the underlying nanotechnologies continue to evolve in the labs of chemists, physicists, and biologists, it has become imperative for computer scientists and engineers to translate the potential of the basic building blocks (analogous to the transistor) emerging from these labs into information systems. Their design will face multiple challenges ranging from the inherent (un)reliability due to the self-assembly nature of the fabrication processes for nanotechnologies, from the complexity due to the sheer volume of nanodevices that will have to be integrated for complex functionality, and from the need to integrate these new nanotechnologies with silicon devices in the same system.
The journal provides comprehensive coverage of innovative work in the specification, design analysis, simulation, verification, testing, and evaluation of computing systems constructed out of emerging technologies and advanced semiconductors. Also of interest are innovations in system design for green and sustainable computing, and computing-driven solutions to emerging areas in biotechnology. Topics include, but are not limited to:

        Logic Primitive Design and Synthesis: how to design computational logic primitives from the new nanotechnologies, and design tools supporting their effective design and verification,

        System-Level Specification, Design and Synthesis: how to interconnect these computational primitives to build complete information systems, and design tools for specifying, synthesizing, and verifying such systems,

        Software-Level Specification, Design and Synthesis: how to develop the necessary software so that applications can be effectively mapped onto information systems implemented using these new nanotechnologies, and tools for generating and verifying the software, and

        Mixed-Technology Systems: how to interface across potentially hybrid nanotechnologies that may co-exist in the same information system.
Last updated by Dou Sun in 2017-03-10
Special Issues
Special Issue on Frontiers of Hardware and Algorithms for On-chip Learning
Submission Date: 2017-03-30

Machine learning algorithms have made significant progress in recent years, achieving accuracy close to, or even better than human-level perception in various tasks, such as image based search, multi-category classification, and scene analysis. However, most of the approaches heavily rely on the availability of large datasets and the time-consuming off-line training to generate an accurate model, which are major limitations in applications with dynamic variations and personalized needs. In addition, the computational complexity of deep learning and computer vision algorithms still challenges the state-of-the-art computing platforms, especially when the application of interest is tightly constrained by the requirements of low power, high throughput, small latency, etc. In recent years, there have been enormous advances in implementing machine learning algorithms with application-specific hardware (e.g., FPGA, ASIC, etc.). There is a timely need to map the latest learning algorithms to physical hardware, in order to achieve orders of magnitude improvement in performance, energy efficiency and compactness. Recent progress in computational neurosciences and nanoelectronic technology, such as resistive memory devices, will further help shed light on future hardware-software platforms for on-chip learning. In 2015 and 2016, the workshop on Hardware and Algorithms for Learning On-a-chip (HALO) were successfully organized, resulting a special issue in JETC in 2016. To address the rapid progress in this field, this new special issue is proposed to report the frontiers of research and practice. The overarching goal of this special issue is to explore the potential of on-chip machine learning, to reveal emerging algorithms and design needs, and to promote novel applications for learning. A holistic approach of concurrent innovations in hardware and algorithms is essential to support real-time information analytics under stringent power constraints in a mobile system. The key topics of interest include, but are not limited to the following: - Hardware acceleration for machine learning - Deep learning with high speed and high power efficiency - Hierarchical learning and classification on a chip - Hardware implementation of sparse coding, feature extraction and personalization - Hardware design practice of the cortical and sensory systems - Nanoelectronic devices, circuits and architectures for neuromorphic computing - Emerging applications of on-chip learning, including mobile computing, automotive vision, etc.
Last updated by Dou Sun in 2017-03-10
Special Issue on Silicon Photonics
Submission Date: 2017-04-30

Computing systems, from HPC and data center to automobile, aircraft, and cellphone, are integrating growing numbers of processors, accelerators, memories, and peripherals to meet the burgeoning performance requirements of new applications under tight cost, energy, thermal, space, and weight constraints. Recent advances in photonics technologies promise ultra-high bandwidth, low latency, and great energy efficiency to alleviate the inter/intra-rack, inter/intra-board, and inter/intra-chip communication bottlenecks in computing systems. Silicon photonics technologies piggyback onto developed silicon fabrication processes to provide viable and cost-effective solutions. Many companies and institutes have been actively developing silicon photonics technologies for more than a decade. A large number of silicon photonics devices and circuits have been demonstrated in CMOS-compatible fabrication processes. Silicon photonics technologies open up new opportunities for applications, architectures, design techniques, and design automation tools to fully explore new approaches and address the challenges of next-generation computing systems. The Special Issue on Silicon Photonics will present the latest progresses and provides insights into the challenges and future developments of this emerging area. The list of topics covered by the special issue includes, but not limited to, the following. - Photonics/optics technology oriented architectures - Integrated photonic/optical switching fabrics - High-radix optical switches for data centers and HPCs - Tools and techniques for optical thermal effects - Tools and techniques for optical crosstalk noises - Tools and techniques for optical process variations - Design automation for photonics/optics technology oriented architectures - Mixed optical-electrical modeling, analysis, and simulation platforms
Last updated by Dou Sun in 2017-03-10
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