Analog Spiking Neuromorphic Circuits and Systems for Brain- and Nanotechnology-Inspired Cognitive Computing

Human society is now facing grand challenges to satisfy the growing demand for computing power, at the same time, sustain energy consumption. By the end of CMOS technology scaling, innovations are required to tackle the challenges in a radically different way. Inspired by the emerging understanding of the computing occurring in a brain and nanotechnology-enabled biological plausible synaptic plasticity, neuromorphic computing architectures are being investigated. Such a neuromorphic chip that combines CMOS analog spiking neurons and nanoscale resistive random-access memory (RRAM) using as electronics synapses can provide massive neural network parallelism, high density and online learning capability, and hence, paves the path towards a promising solution to future energy-efficient real-time computing systems. However, existing silicon neuron approaches are designed to faithfully reproduce biological neuron dynamics, and hence they are incompatible with the RRAM synapses, or require extensive peripheral circuitry to modulate a synapse, and are thus deficient in learning capability. As a result, they eliminate most of the density advantages gained by the adoption of nanoscale devices, and fail to realize a functional computing system. This dissertation describes novel hardware architectures and neuron circuit designs that synergistically assemble the fundamental and significant elements for brain-inspired computing. Versatile CMOS spiking neurons that combine integrate-and-fire, passive dense RRAM synapses drive capability, dynamic biasing for adaptive power consumption, in situ spike-timing dependent plasticity (STDP) and competitive learning in compact integrated circuit modules are presented. Real-world pattern learning and recognition tasks using the proposed architecture were demonstrated with circuit-level simulations. A test chip was implemented and fabricated to verify the proposed CMOS neuron and hardware architecture, and the subsequent chip measurement results successfully proved the idea. The work described in this dissertation realizes a key building block for large-scale integration of spiking neural network hardware, and then, serves as a step-stone for the building of next-generation energy-efficient brain-inspired cognitive computing systems

Hardware Architectures and Implementations for Associative Memories : the Building Blocks of Hierarchically Distributed Memories

During the past several decades, the semiconductor industry has grown into a global industry with revenues around $300 billion. Intel no longer relies on only transistor scaling for higher CPU performance, but instead, focuses more on multiple cores on a single die. It has been projected that in 2016 most CMOS circuits will be manufactured with 22 nm process. The CMOS circuits will have a large number of defects. Especially when the transistor goes below sub-micron, the original deterministic circuits will start having probabilistic characteristics. Hence, it would be challenging to map traditional computational models onto probabilistic circuits, suggesting a need for fault-tolerant computational algorithms. Biologically inspired algorithms, or associative memories (AMs)—the building blocks of cortical hierarchically distributed memories (HDMs) discussed in this dissertation, exhibit a remarkable match to the nano-scale electronics, besides having great fault-tolerance ability. Research on the potential mapping of the HDM onto CMOL (hybrid CMOS/nanoelectronic circuits) nanogrids provides useful insight into the development of non-von Neumann neuromorphic architectures and semiconductor industry. In this dissertation, we investigated the implementations of AMs on different hardware platforms, including microprocessor based personal computer (PC), PC cluster, field programmable gate arrays (FPGA), CMOS, and CMOL nanogrids. We studied two types of neural associative memory models, with and without temporal information. In this research, we first decomposed the computational models into basic and common operations, such as matrix-vector inner-product and k-winners-take-all (k-WTA). We then analyzed the baseline performance/price ratio of implementing the AMs with a PC. We continued with a similar performance/price analysis of the implementations on more parallel hardware platforms, such as PC cluster and FPGA. However, the majority of the research emphasized on the implementations with all digital and mixed-signal full-custom CMOS and CMOL nanogrids. In this dissertation, we draw the conclusion that the mixed-signal CMOL nanogrids exhibit the best performance/price ratio over other hardware platforms. We also highlighted some of the trade-offs between dedicated and virtualized hardware circuits for the HDM models. A simple time-multiplexing scheme for the digital CMOS implementations can achieve comparable throughput as the mixed-signal CMOL nanogrids

Wireless Nano and Molecular Scale Neural Interfacing

Nanoscale circuits and sensors built from silicon nanowires, carbon nanotubes and other devices will require methods for unobtrusive interconnection with the macroscopic world to fully realise their potential; the size of conventional wires precludes their integration into dense, miniature systems. The same wiring problem presents an obstacle in our attempts to understand the brain by means of massively deployed nanodevices, for multiplexed recording and stimulation in vivo. We report on a nanoelectromechanical system that ameliorates wiring constraints, enabling highly integrated sensors to be read in parallel through a single output. Its basis is an effect in piezoelectric nanomechanical resonators that allows sensitive, linear and real-time transduction of electrical potentials. We interface multiple signals through a mechanical Fourier transform using tuneable resonators of different frequency and extract the signals from the system optically. With this method we demonstrate the direct transduction of neuronal action potentials from an extracellular microelectrode. We further extend this approach to incorporate nanophotonics for an all-optical system, coupled via a single optical fibre. Here, the mechanical resonators are both driven and probed optically, but modulated locally by the voltage sensors via the piezoelectric effect. Such piezophotonic nanoelectromechanical systems may be integrated with nanophotonic resonators, allowing concordant multiplexing in both the radiofrequency and optical bandwidths. In principle, this would allow billions of sensor channels to be multiplexed on an optical fibre. With view to eventually integrating such technology into a neural probe, we develop fabrication methods for crafting wired silicon neural probes via photolithography and electron beam lithography. Finally, to complement recording, we propose novel ideas for wireless, multiplexed neural stimulation through the use of radiofrequency-sensitive molecular scale resonators

Memristors

This Edited Volume Memristors - Circuits and Applications of Memristor Devices is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of Engineering. The book comprises single chapters authored by various researchers and edited by an expert active in the physical sciences, engineering, and technology research areas. All chapters are complete in itself but united under a common research study topic. This publication aims at providing a thorough overview of the latest research efforts by international authors on physical sciences, engineering, and technology,and open new possible research paths for further novel developments

Recent patents in bionanotechnologies: nanolithography, bionanocomposites, cell-based computing and entropy production

Recent Patents on Nanotechnology, 2: pp. 1-7This article reviews recent disclosures of bio-inspired, bio-mimicked and bionanotechnologies. Among the patents discussed is a nanoscale porous structure for use in nanocomposites and nanoscale processing. Patents disclosing methods for printing biological materials using nanolithography techniques such as dip-pen technology are discussed, as are patents for optimizing drug design. The relevance of these technologies to disease prevention, disease treatment and disease resistance is discussed. The paper closes with a review of cell-based computing and a brief examination of how information technology has enabled the development of these technologies. Finally a forecast of the how these technologies are likely to accelerate global entropization is discussed as well as a new classification of machine types