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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...
Probabilistic and neural approaches, through their incorporation of nonlinearities and compression of states, enable a broader sampling of the phase space. For a broad set of complex questions that are encountered in conventional computation, this approach is very effective. In these patterns-oriented tasks a fluctuation in the size of data is akin to a thermal fluctuation. A thermodynamic view naturally...
We use a quantum annealing D-Wave 2X (1,152-qubit) computer to generate sparse representations of Canny-filtered, center-cropped 30x30 CIFAR-10 images. Each binary neuron (qubit) represents a feature kernel obtained initially by imprinting on a randomly chosen 5x5 image patch and then adapted via an off-line Hebbian learning protocol using the sparse solutions generated by the D-Wave. When using binary...
This work presents the design and analysis of a mixed-signal neuron (MS-N) for convolutional neural networks (CNN) and compares its performance with a digital neuron (Dig-N) in terms of operating frequency, power and noise. The circuit- level implementation of the MS-N in 65 nm CMOS technology exhibits 2-3 orders of magnitude better energy-efficiency over Dig-N for neuromorphic computing applications...
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 demonstrate multi-level optical weights embedded in a silicon photonic platform based on ferroelectric domain switching. Ferroelectric barium titanate integrated on silicon resonator structures is used as the memory material. By applying short voltage pulses of 100ns, we can switch fractions of the ferroelectric domains and thus change the transmission of the waveguides by more than one order of...
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 focus of the computing industry continues to shift towards designing and building intelligent systems that can handle and learn from large amounts of data. The availability of powerful processing hardware like GPUs and TPUs, has powered the tremendous success of many sophisticated resource intensive machine learning algorithms. However as device scaling and energy dissipation fast approach the...
Convolutional neural networks have become an essential element of spatial deep learning systems. In the prevailing architecture, the convolution operation is performed with Fast Fourier Transforms (FFT) electronically in GPUs. The parallelism of GPUs provides an efficiency over CPUs, however both approaches being electronic are bound by the speed and power limits of the interconnect delay inside the...
Optically active Spatial-Spectral (S2) materials are a unique resource for spectrally based optical memory and processing. At cryogenic temperatures, the rare-earth ions in these materials individually exhibit narrow optical resonances on the order of MHz to sub-KHz, but are inhomogeneously broadened over GHz to THz spectral bandwidths providing up to 10^7 resolvable spectral channels. The material...
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 define some of the programming and system-level challenges facing the application of quantum processing to high-performance computing. Alongside barriers to physical integration, prominent differences in the execution of quantum and conventional programs challenges the intersection of these computational models. Following a brief overview of the state of the art, we discuss recent advances in programming...
Adiabatic logic is an alternative architecture design style to reduce the power consumption of digital cores by using AC power supply instead of DC ones. The energy saving of the digital gates is strongly related to the efficiency of adiabatic AC power supplies. In this paper, we propose a resonant reversible power-clock supply design with four different phases. The resonance deviation between the...
The physical constraints underlying the concept of quantum circuit are considered. In particular it is shown that the point of departure for their modeling starts from the interconnection of the components into a classical network, followed by quantization of the latter, and not by the interconnection of already quantized components. The procedure is straightforward for lossless networks but cannot...
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...
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