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In this paper, we present an on-sensor neuromorphic vision hardware implementation of denoising spatial filter. The mean or median spatial filters with fixed window shape are known for its denoising ability, however, have the drawback of blurring the object edges. The effect of blurring increases with increase in window size. To preserve the edge information, we propose a spatial filter that uses...
A variety of architectures have been proposed for neuromorphic computing chips, including digital, analog, and memristor based approaches. The application space used to analyze these designs is typically narrow, focused primarily on natural signal processing tasks such as image or audio classification. In this work, we analyze the ability of a memristor-based neuromorphic architecture to perform tasks...
Nano-grain reconfigurable cells have the potential to replace memory-consuming LUT (Look-Up Table). However, the cells offering the highest area improvement are also those offering the lowest flexibility, i.e. not all the Boolean functions are available. Reaching the same flexibility of LUT is mandatory to reuse existing FPGA tool flows, which can be obtained by clustering cells in a matrix-like architecture...
Cognitive computing - which learns to do useful computational tasks from data, rather than by being programmed explicitly - represents a fundamentally new form of computing. Unfortunately, Deep Neural Networks (DNNs) learn from repeated exposure to huge datasets, which currently requires extensive computation capabilities (such as many GPUs) working together over days or weeks of time. To accelerate...
DNA is considered as a good computing device because of the predictability of the double helical structure and the Watson-Crick binding thermodynamics associated with them. DNA circuits can be considered as a possible replacement of silicon transistor based circuits, in implantable medical devices, bio-nanorobots, SMART drugs etc. In this paper, we are proposing a novel five input majority logic gate...
Quantum-dot cellular automata (QCA) is a paradigm for low-power, general-purpose, classical computing beyond the transistor era. In classical QCA, the elementary device is a cell, a system of quantum dots with a few mobile charges occupying some dots. Device switching is achieved by quantum mechanical tunneling between dots, and cells are interconnected locally via the electrostatic field. Logic is...
Probabilistic graphical models like Bayesian Networks (BNs) are powerful cognitive-computing formalisms, with many similarities to human cognition. These models have a multitude of real-world applications. New emerging-technology based circuit paradigms leveraging physical equivalence e.g., operating directly on probabilities vs. introducing layers of abstraction, have shown promise in raising the...
The feasibility of using commercial CMOS processes for implementing scalable cryogenic control electronics for universal quantum computers is investigated. Using a systems engineering approach, we break the system down into sub-systems and model the individual components down to transistor level. First results for area demand and power consumption indicate that even with a standard CMOS process, it...
With the death of Moore's law, the computing community is in a period of exploration, focusing on novel computing devices, paradigms, and techniques for programming. The TENN-Lab group has developed a hardware/software co- design framework for this exploration, on which we perform research with three thrusts: (1) Devices for computing, such as memristors and biomimetic membranes. (2) Applications...
Recent advances in the development of commercial quantum annealers such as the D-Wave 2X allow solving NP-hard optimization problems that can be expressed as quadratic unconstrained binary programs. However, the relatively small number of available qubits (around 1000 for the D-Wave 2X quantum annealer) poses a severe limitation to the range of problems that can be solved. This paper explores the...
The prediction of complex signals is among the most important applications of random recurrent Neural Networks (rRNN). Yet, no theory which completely describes prediction in rRNNs exists. As such, these systems remain "black boxes". Based on nearest neighbors theory and random nonlinear mapping, we fully describe the mechanisms employed by rRNNs solving this essential task. Our approach...
The deceleration of transistor feature size scaling has motivated growing adoption of specialized accelerators implemented as GPUs, FPGAs, ASICs, and more recently new types of computing such as neuromorphic, bio-inspired, ultra low energy, reversible, stochastic, optical, quantum, combinations, and others unforeseen. There is a tension between specialization and generalization, with the current state...
Resistive memory (ReRAM) shows promise for use as an analog synapse element in energy-efficient neural network algorithm accelerators. A particularly important application is the training of neural networks, as this is the most computationally-intensive procedure in using a neural algorithm. However, training a network with analog ReRAM synapses can significantly reduce the accuracy at the algorithm...
In the near future, one of the main processes is solving large combinatorial optimization problems. However, the performance growth of von Neumann architecture will slow due to the end of semiconductor scaling. To resolve this problem, we propose an Ising computer that maps the optimization problems to the ground state search of Ising models. We previously proposed a computer that finds the ground...
The goal of this work is to demonstrate the use of an FPGA- based signal processing system linked to the D-Wave 2X quantum computer at Los Alamos National Laboratory. This hybrid system implements an algorithm for detecting wideband RF events (such as lightning). The system is structured around a FPGA Software Defined Radio implementing a signal processing algorithm that converts RF data into a filtered...
In this paper, we propose VoiceHD, a novel speech recognition technique based on brain-inspired hyperdimensional(HD) computing. VoiceHD maps preprocessed voice signals in the frequency domain to random hypervectors and combines them to compute a hypervector (as learned patterns) representing each class. During inference, VoiceHD similarly computes a query hypervector; the classification task is done...
Neuromorphic computing takes inspiration from how the brain works to explore novel computing paradigms. Recently, neuromorphic architectures using spiking neurons were proposed for unsupervised learning of pattern- and feature-based representations. These approaches typically use a common WTA architectural motif of lateral inhibition that introduces competition between the neurons. In this paper,...
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