scholarly journals A Non-Volatile All-Spin Analog Matrix Multiplier: An Efficient Hardware Accelerator for Machine Learning

2021 ◽  
Author(s):  
Supriyo Bandyopadhyay ◽  
Rahnuma Rahman
Keyword(s):  
Machine Learning ◽  
Deep Neural Networks ◽  
Tunnel Junctions ◽  
Optimization Problems ◽  
Maximum Energy ◽  
Hardware Accelerator ◽  
Von Neumann ◽  
Combinatorial Optimization Problems ◽  
The Matrix ◽  
Energy Dissipated

We propose and analyze a compact and <i>non-volatile</i> nanomagnetic (all-spin) analog matrix multiplier performing the multiply-and-accumulate (MAC) operation using two magnetic tunnel junctions – one activated by strain to act as the multiplier, and the other activated by spin-orbit torque pulses to act as a domain wall synapse that performs the operation of the accumulator. Each MAC operation can be performed in ~1 ns and the maximum energy dissipated per operation is ~100 aJ. This provides a very useful hardware accelerator for machine learning (e.g. training of deep neural networks), solving combinatorial optimization problems with Ising type machines, and other artificial intelligence tasks which often involve the multiplication of large matrices. The non-volatility allows the matrix multiplier to be embedded in powerful non-von-Neumann architectures.

scholarly journals A Non-Volatile All-Spin Analog Matrix Multiplier: An Efficient Hardware Accelerator for Machine Learning

2021 ◽  
Author(s):  
Supriyo Bandyopadhyay ◽  
Rahnuma Rahman
Keyword(s):  
Machine Learning ◽  
Deep Neural Networks ◽  
Tunnel Junctions ◽  
Optimization Problems ◽  
Maximum Energy ◽  
Hardware Accelerator ◽  
Von Neumann ◽  
Combinatorial Optimization Problems ◽  
The Matrix ◽  
Energy Dissipated

We propose and analyze a compact and <i>non-volatile</i> nanomagnetic (all-spin) analog matrix multiplier performing the multiply-and-accumulate (MAC) operation using two magnetic tunnel junctions – one activated by strain to act as the multiplier, and the other activated by spin-orbit torque pulses to act as a domain wall synapse that performs the operation of the accumulator. Each MAC operation can be performed in ~1 ns and the maximum energy dissipated per operation is ~100 aJ. This provides a very useful hardware accelerator for machine learning (e.g. training of deep neural networks), solving combinatorial optimization problems with Ising type machines, and other artificial intelligence tasks which often involve the multiplication of large matrices. The non-volatility allows the matrix multiplier to be embedded in powerful non-von-Neumann architectures.

scholarly journals Asymptotic Behavior of Memristive Circuits

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 789
Author(s):  
Francesco Caravelli
Keyword(s):  
Lyapunov Function ◽  
Spin Glasses ◽  
Optimization Problems ◽  
Strong Dependence ◽  
Mean Field ◽  
Stationary Points ◽  
Circuit Topology ◽  
Combinatorial Optimization Problems ◽  
The Matrix ◽  
Memristive Circuits

The interest in memristors has risen due to their possible application both as memory units and as computational devices in combination with CMOS. This is in part due to their nonlinear dynamics, and a strong dependence on the circuit topology. We provide evidence that also purely memristive circuits can be employed for computational purposes. In the present paper we show that a polynomial Lyapunov function in the memory parameters exists for the case of DC controlled memristors. Such a Lyapunov function can be asymptotically approximated with binary variables, and mapped to quadratic combinatorial optimization problems. This also shows a direct parallel between memristive circuits and the Hopfield-Little model. In the case of Erdos-Renyi random circuits, we show numerically that the distribution of the matrix elements of the projectors can be roughly approximated with a Gaussian distribution, and that it scales with the inverse square root of the number of elements. This provides an approximated but direct connection with the physics of disordered system and, in particular, of mean field spin glasses. Using this and the fact that the interaction is controlled by a projector operator on the loop space of the circuit. We estimate the number of stationary points of the approximate Lyapunov function and provide a scaling formula as an upper bound in terms of the circuit topology only.

scholarly journals New Directions in Streaming Algorithms

2020 ◽  
Author(s):  
Ali Vakilian
Keyword(s):  
Machine Learning ◽  
Combinatorial Optimization ◽  
Design Theory ◽  
Large Scale ◽  
Computational Models ◽  
Optimization Problems ◽  
Low Rank ◽  
Set Cover ◽  
Streaming Algorithms ◽  
Combinatorial Optimization Problems

Large volumes of available data have led to the emergence of new computational models for data analysis. One such model is captured by the notion of streaming algorithms: given a sequence of N items, the goal is to compute the value of a given function of the input items by a small number of passes and using a sublinear amount of space in N. Streaming algorithms have applications in many areas such as networking and large scale machine learning. Despite a huge amount of work on this area over the last two decades, there are multiple aspects of streaming algorithms that remained poorly understood, such as (a) streaming algorithms for combinatorial optimization problems and (b) incorporating modern machine learningtechniques in the design of streaming algorithms. In the first part of this thesis, we will describe (essentially) optimal streaming algorithms for set cover and maximum coverage, two classic problems in combinatorial optimization. Next, in the second part, we will show how to augment classic streaming algorithms of the frequency estimation and low-rank approximation problems with machine learning oracles in order to improve their space-accuracy tradeoffs. The new algorithms combine the benefits of machine learning with the formal guarantees available through algorithm design theory.

scholarly journals Optimization of wind solar and battery hybrid renewable system using backtrack search algorithm

2021 ◽  
Vol 24 (3) ◽  
pp. 1269
Author(s):  
Ingudam Chitrasen Meitei ◽  
Rajen Pudur
Keyword(s):  
Power Generation ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Cost Optimization ◽  
Combinatorial Optimization Problems ◽  
Hybrid Combination ◽  
The Matrix ◽  
Electrical Engineers ◽  
Backtrack Search ◽  
Efficiency Cost

<p>Penetration of renewable sources to the grid is always a problem for electrical engineers, apart from reliability and efficiency, cost optimization is also a big concern among them. Wind, solar and battery hybrid combinations (WSB-HPS) are also very common among hybrid systems, but this WSBHPS combines wind and solar energy power generation reduces the charge and discharge time of the battery. Therefore, this system improves the reliability of the power supply by fully utilizing the wind and solar power generation and improves the charging and discharging state of the battery and hence reduces the whole cost as the investment in battery is reduced. backtrack search algorithm (BSA) is the highly efficient and powerful algorithm to solve combinatorial optimization problems. In this paper an attempt is made to optimize the hybrid combination using BSA in the matrix laboratory (MATLAB) environment and comparable study is made using HOMER. A complete optimised data is generated for a particular area in Manipur and reduced cost is suggested.</p>

Sign in / Sign up

Export Citation Format

Share Document