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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...
The modern society is now fully dependent upon technology and the technological approach has brought a revolutionary change in each and every field. This paper proposes a multipurpose robot to be used in the battle field. The robot contains Raspberry Pi which acts as a client, packs a video camera for live video streaming, mapping and gripper for disposal of explosives, a Wi-Fi module for controlling...
Calibrating stochastic biochemical models against experimental insights remains a critical challenge in biological design automation. Stochastic biochemical models incorporate the uncertainty inherent in the system being modeled, thus demanding meticulous calibration techniques. We present an approach for calibrating stochastic biochemical models such that the calibrated model satisfies a given behavioral...
Designing of the High Performance Computing (HPC) is a multidimensional challenge. The power and energy consumption of the HPC system is the identified concerns in the realization of the next generation supercomputers. Achieving Exaflop performance within the 20-megawatt of targeted power consumption is a very daunting task. The effective and intelligent power management is the key to achieving energy...
The drastic increase in the commodity computer and network performance for the last generation has a resultant of faster hardware and more sophisticated software. But, the supercomputers of the current generation are still incapable of solving the current problems in the field of science, engineering, and business. This problems arises as a single machine cannot facilitate the availability of various...
Machines that stores and process data had been in the works for a long time, as the volume of data increased so did the capacity of the machines to handle those data. The notion of big data came into light during the early days of internet when machines not only store and process data but also generate a huge volume of information too. We stared to get very large volumes of dataset beyond the capacity...
We introduce a new compact in-memory computing design for implementing 8-bit addition using eight vertically-stacked nanoscale crossbars of one-diode one-memristor 1D1M switches. Each crossbar in our design only has 5 rows and 4 columns. Hence, the design may be used to fabricate a compact 8-bit adder that meets the size constraint of 50nm χ 50nm χ 50nm imposed by the electrical component of the Feynman...
Nanoscale memristor crossbars provide a natural fabric for in-memory computing and have recently been shown to efficiently perform exact logical operations by exploiting the flow of current through crossbar interconnects. In this paper, we extend the flow-based crossbar computing approach to approximate stochastic computing. First, we show that the natural flow of current through probabilistically-switching...
In this paper, we propose image segmentation by Suprathreshold Stochastic Resonance (SSR) after filtering with anisotropic diffusion (AD). AD, which is used to remove the noise and to preserve the significant detail of image like lines, edges etc. It is followed by SSR, which uses the noise for segmentation of the noisy and blurred color images having different brightness values. This algorithm has...
The characterization of supra-threshold stochastic resonance (SSR) in which we add noise in an array of N elements, has been discussed. Previous work on SSR was carried out for the calculation of cross correlation co-efficient for uniform signal with uniform distributed noise and Gaussian signal with Gaussian distributed noise. But there is always uncertainty about the distribution of real data. So,...
In this paper, we have investigated a novel stochastic resonance & particle swarm optimization (PSO) technique for weak signal detection from noisy signal (weak signal + internal noise). PSO technique is used to determine the optimal amount of noise for weak signal detection. Our proposed work is in Neyman-Pearson framework which maximizes the probability of detection PD for a fixed value of probability...
Energy concerns, the infamous memory wall, and the enormous data deluge of the current big-data age have made the integration of processing and memory elements into a very appealing paradigm. In this paper, we focus on a computation-in-memory solution to the problem of multiplying a set of Boolean matrices, also known as Boolean matrix chain multiplication (BMCM). This is a fundamental computational...
This paper presents a method to determine the scaling factor for different stages in a divider chain(ripple counter) to get the best jitter FoM(minimum jitter times power) with a given divider configuration. An analytic expression for the FoM normalized to the first stage of the divider chain is derived and then optmized to get the best jitter FoM. The analysis shows that scaling down the divider...
Crossbars of nanoscale memristors are being fabricated to serve as high-density non-volatile memory devices. The flow of current through memristor crossbars has been recently used to perform in-memory computations. However, existing approaches based on decision procedures only scale to the simplest circuits such as one-bit adders and other approaches employing decision diagrams produce large crossbar...
The rise of data-intensive computational loads has exposed the processor-memory bottleneck in Von Neumann architectures and has reinforced the need for in-memory computing using devices such as memristors. Existing literature on computing Boolean formula using sneak-paths in nanoscale memristor crossbars has only focussed on short Boolean formula. There are two open questions: (i) Can one synthesize...
Autonomous cyber-physical systems rely on modern machine learning methods such as deep neural networks to control their interactions with the physical world. Testing of such intelligent cyberphysical systems is a challenge due to the huge state space associated with high-resolution visual sensory inputs. We demonstrate how fuzzing the input using patterns obtained from the convolutional filters of...
The visibility of the underwater images is highly affected by two major sources of distortion i.e., light scattering and colour change. The multiple reflection and deflection by particles present in the water cause a decrement in visibility and contrast of the image captured by the camera. Distinct attenuation in different component of colour (R, G, B) consequences the colour change in the image....
Cloud Service Providers always offer Communication services that are flexible, on demand and measured. Multimedia services are used to be delivered through Cloud computing with severe QoS necessities. So there is a recent research is going on as a challenge between researchers, the QoS-aware, cost-efficient selection of data centers, due to these all necessities. Here in this work, there is an optimization...
In this paper, we present two new problems and a theoretical framework that can be used to route information in heterogeneous communication networks. These problems are the cardinality-constrained and interval-constrained paths problems and they consist of finding paths in a network such that cardinality constraints on the number of nodes belonging to different sets of labels are satisfied. We propose...
Proactive recommender systems are intelligent systems that provide (i.e. push) pertinent recommendations to the users based on their current tasks or interests. The recommendation algorithms employed in these systems usually compute similarity score or build up a model offline using training data for producing online recommendations. As training in proactive recommender systems when the availability...
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