A VHDL model of a digi-neocognitron neural network for VLSI

Optical character recognition is useful in many aspects of business. However, the use of conventional computers to provide a solution to this problem has not been very effective. Over the past two decades, researchers have utilized artificial neural networks for optical character recognition with considerable success. One such neural network is the neocognitron, a real-valued, multi-layered hierarchical network that simulates the human visual system. The neocognitron was shown to have the capability for pattern recognition despite variations in size, shape or the presence of deformations from the trained patterns. Unfortunately, the neocognitron is an analog network which prevents it from taking full advantage of the many advances in VLSI technology. Major advances in VLSI technology have been in the digital medium. Therefore, it appears necessary to adapt the neocognitron to an efficient digital neural network if it is to be implemented in VLSI. Recent research has shown that through preprocessing approximations and definition of new model functions, the neocognitron is well suited for implementation in digital VLSI. This thesis uses this methodology to implement a large scale digital neocognitron model. The new model, the digi-neocognitron, uses supervised learning and is trained to recognize ten handwritten numerals with widths of one pixel. The development of the neocognitron and the digi-neocognitron software models, and a comparison of their performance will be discussed. This is followed by the development and simulation of the digital model using the VHSIC Hardware Description Language (VHDL). The VHDL model is used to demonstrate the functionality of the hardware model and to aid in its design. The model functions of the digi-neocognitron are then implemented and simulated for a 1.2 micrometers CMOS process

Spin-Based Neuron Model with Domain Wall Magnets as Synapse

We present artificial neural network design using spin devices that achieves ultra low voltage operation, low power consumption, high speed, and high integration density. We employ spin torque switched nano-magnets for modelling neuron and domain wall magnets for compact, programmable synapses. The spin based neuron-synapse units operate locally at ultra low supply voltage of 30mV resulting in low computation power. CMOS based inter-neuron communication is employed to realize network-level functionality. We corroborate circuit operation with physics based models developed for the spin devices. Simulation results for character recognition as a benchmark application shows 95% lower power consumption as compared to 45nm CMOS design

Concepts, Introspection, and Phenomenal Consciousness: An Information-Theoretical Approach

This essay is a sustained information-theoretic attempt to bring new light on some of the perennial problems in the philosophy of mind surrounding phenomenal consciousness and introspection. Following Dretske (1981), we present and develop an informational psychosemantics as it applies to what we call <em>sensory concepts</em>, concepts that apply, roughly, to so-called secondary qualities of objects. We show that these concepts have a special informational character and semantic structure that closely tie them to the brain states realizing conscious qualitative experiences. We then develop an account of introspection which exploits this special nature of sensory concepts. The result is a new class of concepts, which, following recent terminology, we call <em>phenomenal concepts</em>: these concepts refer to phenomenal experience itself and are the vehicles used in introspection. On our account, the connection between sensory and phenomenal concepts is very tight: it consists in different semantic uses of the same cognitive structures underlying the sensory concepts, like RED. Contrary to widespread opinion, we show that information theory contains all the resources to satisfy internalist intuitions about phenomenal consciousness, while not offending externalist ones. A consequence of this account is that it explains and predicts the so-called conceivability arguments against physicalism on the basis of the special nature of sensory and phenomenal concepts. Thus we not only show why physicalism is not threatened by such arguments, but also demonstrate its strength in virtue of its ability to predict and explain away such arguments in a principled way. However, we take the main contribution of this work to be what it provides in addition to a response to those conceivability arguments, namely, a substantive account of the interface between sensory and conceptual systems and the mechanisms of introspection as based on the special nature of the information flow between them

Cognitive Architecture, Concepts, and Introspection: An Information-Theoretic Solution to the Problem of Phenomenal Consciousness

This essay is a sustained attempt to bring new light to some of the perennial problems in philosophy of mind surrounding phenomenal consciousness and introspection through developing an account of sensory and phenomenal concepts. Building on the information-theoretic framework of Dretske (1981), we present an informational psychosemantics as it applies to what we call sensory concepts, concepts that apply, roughly, to so-called secondary qualities of objects. We show that these concepts have a special informational character and semantic structure that closely tie them to the brain states realizing conscious qualitative experiences. We then develop an account of introspection which exploits this special nature of sensory concepts. The result is a new class of concepts, which, following recent terminology, we call phenomenal concepts: these concepts refer to phenomenal experience itself and are the vehicles used in introspection. On our account, the connection between sensory and phenomenal concepts is very tight: it consists in different semantic uses of the same cognitive structures underlying the sensory concepts, such as the concept of red. Contrary to widespread opinion, we show that information theory contains all the resources to satisfy internalist intuitions about phenomenal consciousness, while not offending externalist ones. A consequence of this account is that it explains and predicts the so-called conceivability arguments against physicalism on the basis of the special nature of sensory and phenomenal concepts. Thus we not only show why physicalism is not threatened by such arguments, but also demonstrate its strength in virtue of its ability to predict and explain away such arguments in a principled way. However, we take the main contribution of this work to be what it provides in addition to a response to those conceivability arguments, namely, a substantive account of the interface between sensory and conceptual systems and the mechanisms of introspection as based on the special nature of the information flow between them

Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition

A neuromorphic chip that combines CMOS analog spiking neurons and memristive synapses offers a promising solution to brain-inspired computing, as it can provide massive neural network parallelism and density. Previous hybrid analog CMOS-memristor approaches required extensive CMOS circuitry for training, and thus eliminated most of the density advantages gained by the adoption of memristor synapses. Further, they used different waveforms for pre and post-synaptic spikes that added undesirable circuit overhead. Here we describe a hardware architecture that can feature a large number of memristor synapses to learn real-world patterns. We present a versatile CMOS neuron that combines integrate-and-fire behavior, drives passive memristors and implements competitive learning in a compact circuit module, and enables in-situ plasticity in the memristor synapses. We demonstrate handwritten-digits recognition using the proposed architecture using transistor-level circuit simulations. As the described neuromorphic architecture is homogeneous, it realizes a fundamental building block for large-scale energy-efficient brain-inspired silicon chips that could lead to next-generation cognitive computing.Comment: This is a preprint of an article accepted for publication in IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol 5, no. 2, June 201

Optical implementations of radial basis classifiers

We describe two optical systems based on the radial basis function approach to pattern classification. An optical-disk-based system for handwritten character recognition is demonstrated. The optical system computes the Euclidean distance between an unknown input and 650 stored patterns at a demonstrated rate of 26,000 pattern comparisons/s. The ultimate performance of this system is limited by optical-disk resolution to 10^11 binary operations/s. An adaptive system is also presented that facilitates on-line learning and provides additional robustness