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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...
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,...
To continue scaling in future and to meet emerging application requirements, revolutionary concepts are required not just on "how can we find better switches to implement computers", but also on "how computers compute logic". Multi-Valued Logic (MVL) provides one such opportunity, since efficient implementation of MVL can allow compact and enhanced information processing and be...
Compared to conventional processors, stochastic computing architectures have strong potential to speed up computation time and to reduce power consumption. We present such an architecture, called Bayesian Machine (BM), dedicated to solving Bayesian inference problems. Given a set of noisy signals provided by low-level sensors, a BM estimates the posterior probability distribution of an unknown target...
Nonlinear dynamics and chaos contribute flexibility and rich, complex behavior to nonlinear systems. Transistors and transistor circuits are inherently nonlinear. It was demonstrated that this nonlinearity and the flexibility that comes with it can be utilized to implement flexible, reconfigurable computing, and such approaches are called Nonlinear Dynamics-Based Computing. In nonlinear dynamics-based...
We present a new non-von Neumann architecture, termed "Superstrider," predicated on no more than current projected improvements in semiconductor components and 3D manufacturing technologies, which should offer orders of magnitude advances in both energy efficiency and performance for many high-utility problem classes. The architecture is described, which is based on computing on row-wide...
Evolution-in-materio is a form of unconventional computing combining materials' training and evolutionary search algorithms. In previous work, a mixture of single-walled-carbon-nanotubes (SWCNTs) dispersed in a liquid crystal (LC) was trained so that its morphology and electrical properties were gradually changed to perform a computational task. Material-based computation is treated as an optimisation...
We present an energy-efficient on-chip reconfigurable computing architecture, the so-called OLUT, which is an optical core implementation of a lookup table. It offers significant improvement with respect to optical directed logic architectures, through allowing the use of wavelength division multiplexing (WDM) for computation parallelism. We performed a design space exploration that elucidates the...
The nearest neighbor (NN) algorithm has been used in a broad range of applications including pattern recognition, classification, computer vision, databases, etc. The NN algorithm tests data points to find the nearest data to a query data point. With the Internet of Things the amount of data to search through grows exponentially, so we need to have more efficient NN design. Running NN on multicore...
Considerable efforts have been devoted to the design of low-power digital electronics. However, after decades of improvements and maturation, CMOS technology could face an efficiency ceiling. This is due to the trade-off between leakage and conduction losses inherent to transistors. Consequently, the lowest dissipation per operation remains nowadays few decades higher than the theoretical Landauer''s...
Most existing concepts for hardware implementation of reversible computing invoke an adiabatic computing paradigm, in which individual degrees of freedom (e.g., node voltages) are synchronously transformed under the influence of externally- supplied driving signals. But distributing these "power/clock" signals to all gates within a design while efficiently recovering their energy is difficult...
We live in interesting times. Our systems have unprecedented levels of device integration. Analog and mixed signal components and devices form increasingly large parts of our designs built for low power and high flexibility. New architectures and models of computation that embrace variation like neuromorphic computing are a part of our horizon. Architectures specialized for neural networks and learning...
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...
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...
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 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...
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...
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