Advances in Neuromorphic Hardware Exploiting Emerging Nanoscale Devices (Cognitive Systems Monographs)

This book covers all major aspects of cutting-edge research in the field of neuromorphic hardware engineering involving emerging nanoscale devices. Special emphasis is given to leading works in hybrid low-power CMOS-Nanodevice design. The book offers readers a bidirectional (top-down and bottom-up) perspective on designing efficient bio-inspired hardware. At the nanodevice level, it focuses on various flavors of emerging resistive memory (RRAM) technology. At the algorithm level, it addresses optimized implementations of supervised and stochastic learning paradigms such as: spike-time-dependent plasticity (STDP), long-term potentiation (LTP), long-term depression (LTD), extreme learning machines (ELM) and early adoptions of restricted Boltzmann machines (RBM) to name a few. The contributions discuss system-level power/energy/parasitic trade-offs, and complex real-world applications. The book is suited for both advanced researchers and students interested in the field.

本書涵蓋了神經形態硬體工程領域中新興納米級裝置的尖端研究的所有主要方面。特別強調混合低功耗CMOS-納米裝置設計的領先作品。本書為讀者提供了一個雙向（自上而下和自下而上）的設計高效仿生硬體的觀點。在納米裝置層面上，它專注於各種新興阻性記憶體（RRAM）技術。在算法層面上，它涉及優化實施監督和隨機學習範式，例如：脈衝時間相依性塑性（STDP）、長期增強（LTP）、長期抑制（LTD）、極限學習機（ELM）和早期採用受限玻爾茨曼機（RBM）等。這些貢獻討論了系統級的功率/能量/寄生效應的權衡，以及複雜的現實應用。本書適合對該領域感興趣的高級研究人員和學生閱讀。