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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...
We describe an effort to simulate the neural pathway from the inner ear (cochlea) to the primary auditory cortex in the brain. The human cochlea contains sensory cells (inner hair cells), which respond to the mechanical motion of traveling waves that sweep along the basilar membrane. Neurons triggered by the sensory cells carry sound signals from the cochlea to the brain through a series of a half-dozen...
According to International Roadmap for Devices and Systems (IRDS), after 2024, there is no headroom for 2D geometry scaling. By 2024, IRDS predicts that monolithic 3D integration technology will be one of the most critical performance boosters. This prediction follows the highly promising and relatively recent improvements on the fabrication of second-tier devices on a single substrate. In this study,...
Gene Expression Networks (GENs) attempt to model how genetic information stored in the DNA (Genotype) results in the synthesis of proteins, and consequently, the physical traits of an organism (Phenotype). Deciphering GENs plays an important role in a wide range of applications from genetic studies of the origins of life to personalized healthcare. Probabilistic graphical models such as Bayesian Networks...
Photonic reservoir computing is a recent bio- inspired paradigm for signal processing. Despite first successes, the paradigm still faces challenges. We address some of these challenges and introduce our approaches to solve them. In detail, we discuss how integrated reservoirs can be scaled up by injecting multiple copies of the input. Further we introduce a new hardware-friendly training method for...
We have been experiencing two very important movements in computing. On the one hand, a tremendous amount of resource has been invested into innovative applications such as first-principle-based methods, deep learning and cognitive computing. On the other hand, the industry has been taking a technological path where application performance and energy efficiency vary by more than two orders of magnitude...
Forthcoming CMOS technology nodes are in principle sufficient for achieving both the quantum information density and the speed that are critical for error-free logical qubits. Using data from the roadmap for semiconductor devices from ITRS and IEDM, we applied the standard CMOS design rules to a universal set of quantum logic gates to control silicon qubits. We consequently obtain a scaling law for...
Architectural contributions to energy dissipation in von Neumann and non-von Neumann processors is explored at a fundamental thermodynamic level. Technology agnostic lower bounds on dissipation resulting from irreversible information loss, obtained from a processor thermodynamics methodology, are analyzed and compared for a programmed general-purpose von Neumann processor and a special-purpose cellular...
Lightweight cryptography (LWC) provides cryptographic solutions for resource-constrained devices such as RFID tags, industrial controllers, sensor nodes, and smart cards. LWC based devices have stringent constraints on power consumption and are vulnerable to side-channel attacks such as Differential Power Analysis (DPA). The existing CMOS-based countermeasures for DPA are not suitable for circuits...
The architecture, operation, and characteristics of two post-CMOS reconfigurable fabrics are identified to realize energy-sparing and resilience features, while remaining feasible for near-term fabrication. First, Storage Cell Replacement Fabrics (SCRFs) provide a reconfigurable computing platform utilizing near- zero leakage Spin Hall Effect devices which replace SRAM bit-cells within Look-Up Tables...
Mimicking the collaborative behavior of biological swarms, such as bird flocks and ant colonies, Swarm Intelligence algorithms provide efficient solutions for various optimization problems. On the other hand, a computational model of the human brain, spiking neural networks, has been showing great promise in recognition, inference, and learning, due to recent emergence of neuromorphic hardware for...
Recent experimental work has demonstrated nano- textured magnetic Josephson junctions (MJJs) that exhibit tunable spiking behavior with ultra-low training energies in the attojoule range. MJJ devices integrated with standard single-flux-quantum neural systems form a new class of neuromorphic technologies that have spiking energies between attojoules and zeptojoules, operation frequencies up to 100...
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...
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...
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...
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...
In the past several decades on-chip dimensions have scaled over 2000X, while dimensions on printed circuit board have scaled 4-5X. This modest scaling of packaging dimensions has severely limited system scaling. To address this, we have proposed a disruptive package-free integration scheme. We replace the traditional organic printed circuit board (PCB) with silicon interconnect fabric (SiIF) and replace...
Rebooting computing using in-memory architectures relies on the ability of emerging devices to execute a legacy software stack. In this paper, we present our approach of executing compute kernels written in a subset of the C programming language using flow-based computing on nanoscale memristor crossbars. Our approach also tests the correctness of the design using the parallel Xyces electronic simulation...
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...
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