Energy-Aware Neuromorphic Hardware
A special issue of Journal of Low Power Electronics and Applications (ISSN 2079-9268).
Deadline for manuscript submissions: closed (20 June 2018) | Viewed by 11075
Special Issue Editors
Interests: artificial intelligence (AI); embedded hardware; neuromorphic computing; nano-scale computing system with novel silicon and post-silicon devices, and low-power digital and mixed-signal CMOS circuit design
Special Issues, Collections and Topics in MDPI journals
Interests: computer architecture with emphasis on reconfigurable logic devices, evolvable hardware, and emerging devices
Special Issues, Collections and Topics in MDPI journals
Interests: processor architecture; high-performance computing; AI-based IoT; underwater drones; cultural heritage preservation and protection
Special Issues, Collections and Topics in MDPI journals
Interests: hardware/hardware-assisted security; artificial intelligence security; integrated circuit design; post-quantum cryptographic acceleration
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
During recent years, researchers throughout academia and industry have been advancing the theory, operation, and applications of neuromorphic computing systems. Recent interest in neuromorphic computing systems stems from its superior and rapidly advancing performance at tasks such as image recognition, learning of complex intelligent behaviors, and large-scale information retrieval problems such as intelligent web search. However, to attain the benefits of neuromorphic computing, high computational and energy-consumption demands of the underlying processing, interconnect, and memory devices on which software-based neuromorphic computing executes has become an intense focus of government, industry, and academic research. Innovative hardware implementations are sought to attain throughput goals within area, security, and energy constraints for orders of magnitude improvements via innovations across the hardware stack. This Special Issue of the JLPEA is dedicated to advances in all aspects of Energy and Secured-Aware Neuromorphic Hardware. We invite original submissions advancing device, circuits, and hardware architectures of neuromorphic computing systems. Topics of interest include (but are not limited to) the following:
- (a) Emerging low-power technologies and circuits for neuromorphic computing, including post-CMOS devices;
- (b) Comparison studies between neuromorphic hardware architectures, spanning use of emerging devices, GPUs, TPUs, ASICs, etc.;
- (c) Hardware microarchitectures and implementations for neuromorphic computing;
- (d) Modeling, optimizations, and retraining neuromorphic systems in hardware;
- (e) Hardware implementations targeting constrained environments, such as edge computing and IoT;
- (f) Secure neuromorphic applications on FPGAs and ASICs;
- (g) Applications of neuromorphic-enabled hardware, such as communications systems, big data processing, smart cities, and medical applications;
- (h) Neuromorphic inspired hardware security.
Dr. Yu Bai
Dr. Ronald F. DeMara
Dr. Meng Lin
Dr. Jiliang Zhang
Guest Editors
Manuscript Submission Information
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Keywords
- Low power neuromorphic systems in hardware
- Post-CMOS device neuromorphic systems
- Security of neuromorphic systems
- Applications of Neuromorphic systems in hardware
- EDA tools and algorithms for neuromorphic systems
- Neuromorphic inspired hardware security
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