GDOT: A graphene-based nanofunction for dot-product computation

2016 ◽  
Author(s):  
Ning C. Wang ◽  
Sujan K. Gonugondla ◽  
Ihab Nahlus ◽  
Naresh R. Shanbhag ◽  
Eric Pop
Keyword(s):  
Dot Product

Black-white contrast of dislocation loops

1978 ◽  
Vol 36 (1) ◽  
pp. 328-329
Author(s):  
J. J. Hren ◽  
W. D. Cooper ◽  
L. J. Sykes
Keyword(s):  
Electron Microscopy ◽  
Transmission Electron Microscopy ◽  
Dislocation Loops ◽  
Major Premise ◽  
Burgers Vector ◽  
Transmission Electron ◽  
Primary Means ◽  
Contrast Analysis ◽  
Dot Product ◽  
Imaging Conditions

Small dislocation loops observed by transmission electron microscopy exhibit a characteristic black-white strain contrast when observed under dynamical imaging conditions. In many cases, the topography and orientation of the image may be used to determine the nature of the loop crystallography. Two distinct but somewhat overlapping procedures have been developed for the contrast analysis and identification of small dislocation loops. One group of investigators has emphasized the use of the topography of the image as the principle tool for analysis. The major premise of this method is that the characteristic details of the image topography are dependent only on the magnitude of the dot product between the loop Burgers vector and the diffracting vector. This technique is commonly referred to as the (g•b) analysis. A second group of investigators has emphasized the use of the orientation of the direction of black-white contrast as the primary means of analysis.

scholarly journals Sphere and Dot Product Representations of Graphs

2012 ◽  
Vol 47 (3) ◽  
pp. 548-568 ◽  
Author(s):  
Ross J. Kang ◽  
Tobias Müller
Keyword(s):  
Dot Product

On the design of modulo 2n+1 dot product and generalized multiply–add units

2013 ◽  
Vol 39 (2) ◽  
pp. 410-419 ◽  
Author(s):  
C. Efstathiou ◽  
N. Moschopoulos ◽  
I. Voyiatzis ◽  
K. Pekmestzi
Keyword(s):  
Dot Product

scholarly journals Accelerating Spike-by-Spike Neural Networks on FPGA with Hybrid Custom Floating-Point and Logarithmic Dot-Product Approximation

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Yarib Nevarez ◽  
David Rotermund ◽  
Klaus R. Pawelzik ◽  
Alberto Garcia-Ortiz
Keyword(s):  
Neural Networks ◽  
Floating Point ◽  
Dot Product

Quantum-Dot Transistor Based Multi-Bit Multiplier Unit for In-Memory Computing

2020 ◽  
Vol 29 (01n04) ◽  
pp. 2040007
Author(s):  
Yang Zhao ◽  
Fengyu Qian ◽  
Faquir Jain ◽  
Lei Wang
Keyword(s):  
Quantum Dot ◽  
Data Processing ◽  
High Performance ◽  
Memory Cell ◽  
Process Variations ◽  
High Energy ◽  
Data Movement ◽  
Neuron Networks ◽  
Fast Processing ◽  
Dot Product

In-memory computing is an emerging technique to fulfill the fast growing demand for high-performance data processing. This technique provides fast processing and high throughput by accessing data stored in the memory array rather than dealing with complicated operation and data movement on hard drive. For data processing, the most important computation is dot product, which is also the core computation for applications such as deep learning neuron networks, machine learning, etc. As multiplication is the key function in dot product, it is critical to improve its performance and achieve faster memory processing. In this paper, we present a design with the ability to perform in-memory multi-bit multiplications. The proposed design is implemented by using quantum-dot transistors, which enable multi-bit computations in the memory cell. Experimental results demonstrate that the proposed design provides reliable in-memory multi-bit multiplications with high density and high energy efficiency. Statistical analysis is performed using Monte Carlo simulations to investigate the process variations and error effects.

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