Pre-Calculating Ising Memory: Low Cost Method to Enhance Traditional Memory with Ising Ability

Zhang, Jian; Chen, Shuming; Wang, Zhi; Wang, Lei; Lv, Linghui; Wang, Yaohua

doi:10.1109/iscas.2018.8350988

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…The CMOS implementations [16], [33], [34], [35], [36], [37] are easy to integrate and expand, which means that it is a more suitable strategy for mapping and solving large-scale practical Ising model problem. In application, CMOS Ising machine has advantages such as tiny size, exible expansion, high integration, low system power consumption and so on [36].…”

Section: Introductionmentioning

confidence: 99%

“…In application, CMOS Ising machine has advantages such as tiny size, exible expansion, high integration, low system power consumption and so on [36]. Most CMOS chips are based on nonfully connected structures, like lattice graph [15], [33], [35], [36], king graph [34], [38], [39], [40], and Hexagonally graph [41]. All-to-all connected Ising models are more practical value than sparse ones, but communication and synchronization between the spins degrade the speed performance in CMOS [16].…”

Section: Introductionmentioning

confidence: 99%

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Point convolutional neural network algorithm for Ising model ground state research based on spring vibration

Jiang

Chen

Qiao

et al. 2023

Preprint

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The ground state search of the Ising model can be used to solve many combinatorial optimization problems. Under the current computer architecture, an Ising ground state search algorithm suitable for hardware computing is necessary for solving practical problems. Inspired by the potential energy conversion of springs, we propose a point convolutional neural network algorithm for ground state search based on spring vibration model, called Spring-Ising Algorithm. Spring-Ising Algorithm regards the spin as a moving mass point connected to a spring and establish the equation of motion for all spins. Spring-Ising Algorithm can be mapped on the GPU or AI chips through the basic structure of the neural network for fast and efficient parallel computing. The algorithm has very productive results for solving the Ising model and has been test in the recognized test benchmark K2000. The algorithm introduces the concept of dynamic equilibrium to achieve a more detailed local search by dynamically adjusting the weight of the Ising model in the spring oscillation model. Finally, there is the simple hardware test speed evaluation. Spring-Ising Algorithm can provide the possibility to calculate the Ising model on a chip which focuses on accelerating neural network calculations.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Point convolutional neural network algorithm for Ising model ground state research based on spring vibration

Jiang

Chen

Qiao

et al. 2023

Preprint

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“…The Ising chip [10][11][12][13][14][15], based on the Ising model [16], is one of the new architectures, which can efficiently solve combinatorial optimization problem. Combinatorial optimization is a branch of discrete mathematics that seeks to find the best possible solution from a finite or countably set of possibilities [17].…”

Section: Introductionmentioning

confidence: 99%

AIM: Annealing in Memory for Vision Applications

Wang

Zhang

et al. 2020

Symmetry

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As the Moore’s law era will draw to a close, some domain-specific architectures even non-Von Neumann systems have been presented to keep the progress. This paper proposes novel annealing in memory (AIM) architecture to implement Ising calculation, which is based on Ising model and expected to accelerate solving combinatorial optimization problem. The Ising model has a symmetrical structure and realizes phase transition by symmetry breaking. AIM draws annealing calculation into memory to reduce the cost of information transfer between calculation unit and the memory, improves the ability of parallel processing by enabling each Static Random-Access Memory (SRAM) array to perform calculations. An approximate probability flipping circuit is proposed to avoid the system getting trapped in local optimum. Bit-serial design incurs only an estimated 4.24% area above the SRAM and allows the accuracy to be easily adjusted. Two vision applications are mapped for acceleration and results show that it can speed up Multi-Object Tracking (MOT) by 780× and Multiple People Head Detection (MPHD) by 161× with only 0.0064% and 0.031% energy consumption respectively over approximate algorithms.

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Point convolutional neural network algorithm for Ising model ground state research based on spring vibration

Jiang,

Chen,

Qiao

et al. 2024

Sci Rep

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The ground state search of the Ising model can be used to solve many combinatorial optimization problems. Under the current computer architecture, an Ising ground state search algorithm suitable for hardware computing is necessary for solving practical problems. Inspired by the potential energy conversion of the springs, we propose the Spring-Ising Algorithm, a point convolutional neural network algorithm for ground state search based on the spring vibration model. Spring-Ising Algorithm regards the spin as a moving mass point connected to a spring and establishes the equation of motion for all spins. Spring-Ising Algorithm can be mapped on AI chips through the basic structure of the neural network for fast and efficient parallel computing. The algorithm has shown promising results in solving the Ising model and has been tested in the recognized test benchmark K2000. The optimal results of this algorithm after 10,000 steps of iteration are 2.9% of all results. The algorithm introduces the concept of dynamic equilibrium to achieve a more detailed local search by dynamically adjusting the weight of the Ising model in the spring oscillation model. Spring-Ising Algorithm offers the possibility to calculate the Ising model on a chip which focuses on accelerating neural network calculations.

show abstract

Pre-Calculating Ising Memory: Low Cost Method to Enhance Traditional Memory with Ising Ability

Cited by 5 publications

References 13 publications

Point convolutional neural network algorithm for Ising model ground state research based on spring vibration

Point convolutional neural network algorithm for Ising model ground state research based on spring vibration

AIM: Annealing in Memory for Vision Applications

Point convolutional neural network algorithm for Ising model ground state research based on spring vibration

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