Computer Science

A novel a priori Monte Carlo (APMC) algorithm is proposed to accurately simulate the molecules absorbed at spherical receiver(s) with low computational complexity in diffusion-based molecular communication (MC) systems. It is demonstrated that the APMC algorithm achieves high simulation efficiency since by using this algorithm, the fraction of molecules absorbed for a relatively large time step length precisely matches the analytical result. Therefore, the APMC algorithm overcomes the shortcoming of the existing refined Monte Carlo (RMC) algorithm which enables accurate simulation for a relatively small time step length only. Moreover, for the RMC algorithm, an expression is proposed to quickly predict the simulation accuracy as a function of the time step length and system parameters, which facilitates the choice of simulation time step for a given system. Furthermore, a rejection threshold is proposed for both the RMC and APMC algorithms to significantly save computational complexity while causing an extremely small loss in accuracy.

This work presents a novel general compact model for 7nm technology node devices like FinFETs. As an extension of previous conventional compact model that based on some less accurate elements including one-dimensional Poisson equation for three-dimensional devices and analytical equations for short channel effects, quantum effects and other physical effects, the general compact model combining few TCAD calibrated compact models with statistical methods can eliminate the tedious physical derivations. The general compact model has the advantages of efficient extraction, high accuracy, strong scaling capability and excellent transfer capability. As a demo application, two key design knobs of FinFET and their multiple impacts on RC control ESD power clamp circuit are systematically evaluated with implementation of the newly proposed general compact model, accounting for device design, circuit performance optimization and variation control. The performance of ESD power clamp can be improved extremely. This framework is also suitable for pathfinding researches on 5nm node gate-all-around devices, like nanowire (NW) FETs, nanosheet (NSH) FETs and beyond.

Ant colony optimization is one of the potential solutions to tackle intractable NP-Hard discrete combinatorial optimization problems. The metaphor of ant colony can be thought of as the evolution of the best path from a given graph as a globally optimal solution, which is unaffected by earlier local convergence to achieve improved optimization efficiency. Earlier Quantum Ant Colony Optimization research work was primarily based on Quantum-inspired Evolutionary Algorithms, which deals with customizing and improving the quantum rotation gate through upgraded formation of the lookup table of rotation angle. Instead of relying on evolutionary algorithms, we have proposed a discrete-time quantum algorithm based on adaptive quantum circuit for pheromone updation. The algorithm encodes all possible paths in the exhaustive search space as input to the ORACLE. Iterative model of exploration and exploitation of all possible paths by quantum ants results in global optimal path convergence through probabilistic measurement of selected path. Our novel approach attempts to accelerate the search space exploitation in a significant manner to obtain the best optimal path as a solution through quantum arallelization achieving polynomial time speed-up over its classical counter part.

Stochastic computing (SC) presents high error tolerance and low hardware cost, and has great potential in applications such as neural networks and image processing. However, the bitstream generator, which converts a binary number to bitstreams, occupies a large area and energy consumption, thus weakening the superiority of SC. In this paper, we propose a novel technique for generating bitstreams in parallel, which needs only one clock for conversion and significantly reduces the hardware cost. Synthesis results demonstrate that the proposed parallel bitstream generator improves 2.5x area and 712x energy consumption.

Spiking Neural Networks (SNNs) offer an event-driven and more biologically realistic alternative to standard Artificial Neural Networks based on analog information processing. This can potentially enable energy-efficient hardware implementations of neuromorphic systems which emulate the functional units of the brain, namely, neurons and synapses. Recent demonstrations of ultra-fast photonic computing devices based on phase-change materials (PCMs) show promise of addressing limitations of electrically driven neuromorphic systems. However, scaling these standalone computing devices to a parallel in-memory computing primitive is a challenge. In this work, we utilize the optical properties of the PCM, Ge\textsubscript{2}Sb\textsubscript{2}Te\textsubscript{5} (GST), to propose a Photonic Spiking Neural Network computing primitive, comprising of a non-volatile synaptic array integrated seamlessly with previously explored `integrate-and-fire' neurons. The proposed design realizes an `in-memory' computing platform that leverages the inherent parallelism of wavelength-division-multiplexing (WDM). We show that the proposed computing platform can be used to emulate a SNN inferencing engine for image classification tasks. The proposed design not only bridges the gap between isolated computing devices and parallel large-scale implementation, but also paves the way for ultra-fast computing and localized on-chip learning.

In this paper, we propose a new end-to-end system for wired nano-communication networks using a self-assembled polymer. The self-assembly of a polymer creates a channel between the transmitter and the receiver in the form of a conductive nanowire that uses electrons as carriers of information. We derive the channel's analytical model and its master equation to study the dynamic process of the polymer self-assembly. We validate the analytical model with numerical and Monte-Carlo simulations. Then, we approximate the master equation by a one-dimensional Fokker-Planck equation and we solve this equation analytically and numerically. We formulate the expressions of the polymer elongation rate, its diffusion coefficient and the nullcline to study the distribution and the stability of the self-assembled nanowire. This study shows promising results for realizing stable polymer-based wired nanonetworks that can achieve high throughput.

Gaussian Processes are used in many applications to model spatial phenomena. Within this context, a key issue is to decide the set of locations where to take measurements so as to obtain a better approximation of the underlying function. Current state of the art techniques select such set to minimize the posterior variance of the Gaussian process. We explore the feasibility of solving this problem by proposing a novel Quadratic Unconstrained Binary Optimization (QUBO) model. In recent years this QUBO formulation has gained increasing attention since it represents the input for the specialized quantum annealer D-Wave machines. Hence, our contribution takes an important first step towards the sampling optimization of Gaussian processes in the context of quantum computation. Results of our empirical evaluation shows that the optimum of the QUBO objective function we derived represents a good solution for the above mentioned problem. In fact we are able to obtain comparable and in some cases better results than the widely used submodular technique.

The subset sum problem is a typical NP-complete problem that is hard to solve efficiently in time due to the intrinsic superpolynomial-scaling property. Increasing the problem size results in a vast amount of time consuming in conventionally available computers. Photons possess the unique features of extremely high propagation speed, weak interaction with environment and low detectable energy level, therefore can be a promising candidate to meet the challenge by constructing an a photonic computer computer. However, most of optical computing schemes, like Fourier transformation, require very high operation precision and are hard to scale up. Here, we present a chip built-in photonic computer to efficiently solve the subset sum problem. We successfully map the problem into a waveguide network in three dimensions by using femtosecond laser direct writing technique. We show that the photons are able to sufficiently dissipate into the networks and search all the possible paths for solutions in parallel. In the case of successive primes the proposed approach exhibits a dominant superiority in time consumption even compared with supercomputers. Our results confirm the ability of light to realize a complicated computational function that is intractable with conventional computers, and suggest the subset sum problem as a good benchmarking platform for the race between photonic and conventional computers on the way towards "photonic supremacy".

In this paper, spin-orbit torque (SOT) magnetoresistive random-access memory (MRAM) devices are leveraged to realize sigmoidal neurons and binarized synapses for a single-cycle analog in-memory computing (IMC) architecture. First, an analog SOT-MRAM-based neuron bitcell is proposed which achieves a 12x reduction in power-area-product compared to the previous most power- and area-efficient analog sigmoidal neuron design. Next, proposed neuron and synapse bit cells are used within memory subarrays to form an analog IMC-based multilayer perceptron (MLP) architecture for the MNIST pattern recognition application. The architecture-level results exhibit that our analog IMC architecture achieves at least two and four orders of magnitude performance improvement compared to a mixed-signal analog/digital IMC architecture and a digital GPU implementation, respectively while realizing a comparable classification accuracy.

Synthetic molecular communication (MC) is a new communication engineering paradigm which is expected to enable revolutionary applications such as smart drug delivery and real-time health monitoring. The design and implementation of synthetic MC systems (MCSs) at nano- and microscale is very challenging. This is particularly true for synthetic MCSs employing biological components as transmitters and receivers or as interfaces with natural biological MCSs. Nevertheless, since such biological components have been optimized by nature over billions of years, using them in synthetic MCSs is highly promising. This paper provides a survey of biological components that can potentially serve as the main building blocks, i.e., transmitter, receiver, and signaling particles, for the design and implementation of synthetic MCSs. Nature uses a large variety of signaling particles of different sizes and with vastly different properties for communication among biological entities. Here, we focus on three important classes of signaling particles: cations (specifically protons and calcium ions), neurotransmitters (specifically acetylcholine, dopamine, and serotonin), and phosphopeptides. For each of these candidate signaling particles, we present several specific transmitter and receiver structures mainly built upon proteins that are capable of performing the distinct physiological functionalities required from the transmitters and receivers of MCSs. Moreover, we present options for both microscale implementation of MCSs as well as the micro-to-macroscale interfaces needed for experimental evaluation of MCSs. Furthermore, we outline new research directions for the implementation and the theoretical design and analysis of the proposed transmitter and receiver architectures.