Spiking analog VLSI neuron assemblies as constraint satisfaction problem solvers

Binas, Jonathan; Indiveri, Giacomo; Pfeiffer, Michael

doi:10.1109/iscas.2016.7538992

Cited by 19 publications

(12 citation statements)

References 11 publications

(19 reference statements)

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“…For a neuromorphic CSP solver to be viable, the SNN must not only visit energetically minimum states but also detect when one is visited. Previous neuromorphic hardware implementations [89]- [96] required a von Neumann processor to continuously read out the entire high-dimensional network state S to evaluate the cost function and identify solutions. This leads to an impractical off-chip communication bottleneck that only worsens with increasing problem size.…”

Section: (E) Decomposition Of the Time To Solution T Into The Number Of Time Steps To Solution T (Left) And Mean Time Per Timestep τ (Rigmentioning

confidence: 99%

Advancing Neuromorphic Computing With Loihi: A Survey of Results and Outlook

et al. 2021

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Deep artificial neural networks apply principles of the brain's information processing that led to breakthroughs in machine learning spanning many problem domains. Neuromorphic computing aims to take this a step further to chips more directly inspired by the form and function of biological neural circuits, so they can process new knowledge, adapt, behave, and learn in real time at low power levels. Despite several decades of research, until recently, very few published results have shown that today's neuromorphic chips can demonstrate quantitative computational value. This is now changing with the advent of Intel's Loihi, a neuromorphic research processor designed to support a broad range of spiking neural networks with sufficient scale, performance, and features to deliver competitive results compared to state-of-the-art contemporary computing architectures. This survey reviews results that are obtained to date with Loihi across the major algorithmic domains under study, including deep learning approaches and novel approaches that aim to more directly harness the key features of spike-based neuromorphic hardware. While conventional feedforward deep neural networks show modest if any benefit on Loihi, more brain-inspired networks using recurrence, precise spike-timing relationships, synaptic plasticity, stochasticity, and sparsity perform certain computation with orders of magnitude lower latency and energy compared to state-of-the-art conventional approaches. These compelling neuromorphic networks solve a diverse range of problems representative of brain-like computation, such as event-based

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Section: (E) Decomposition Of the Time To Solution T Into The Number Of Time Steps To Solution T (Left) And Mean Time Per Timestep τ (Rigmentioning

confidence: 99%

Advancing Neuromorphic Computing With Loihi: A Survey of Results and Outlook

et al. 2021

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“…Very recent design ideas for spike-based neuromorphic hardware that is able to solve constraint satisfaction problems can be found in [19] and [20].…”

Section: Solving Constraint Satisfaction Problemsmentioning

confidence: 99%

To Spike or Not to Spike: That Is the Question

Maass

2015

Proc. IEEE

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“…Each layer corresponds to a possible number in the puzzle, e.g., ''1'' or ''2'' for the 2 × 2 Sudoku. The neurons can thus be rearranged in an N × N × N matrix [23], where the entry corresponding to ''1'' indicates a firing neuron and the entry corresponding to ''0'' indicates a silent neuron. Fig.…”

Section: Hardware Solution Of a Sudoku Puzzlementioning

confidence: 99%

A Spiking Recurrent Neural Network With Phase-Change Memory Neurons and Synapses for the Accelerated Solution of Constraint Satisfaction Problems

Pedretti

Mannocci

Hashemkhani

et al. 2020

IEEE J. Explor. Solid-State Comput. Devices Circuits

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Data-intensive computing applications, such as object recognition, time series prediction, and optimization tasks, are becoming increasingly important in several fields, including smart mobility, health, and industry. Because of the large amount of data involved in the computation, the conventional von Neumann architecture suffers from excessive latency and energy consumption due to the memory bottleneck. A more efficient approach consists of in-memory computing (IMC), where computational operations are directly carried out within the data. IMC can take advantage of the rich physics of memory devices, such as their ability to store analog values to be used in matrix-vector multiplication (MVM) and their stochasticity that is highly valuable in the frame of optimization and constraint satisfaction problems (CSPs). This article presents a stochastic spiking neuron based on a phase-change memory (PCM) device for the solution of CSPs within a Hopfield recurrent neural network (RNN). In the RNN, the PCM cell is used as the integrating element of a stochastic neuron, supporting the solution of a typical CSP, namely a Sudoku puzzle in hardware. Finally, the ability to solve Sudoku puzzles using RNNs with PCM-based neurons is studied for increasing size of Sudoku puzzles by a compact simulation model, thus supporting our PCM-based RNN for data-intensive computing. INDEX TERMS Phase change memory (PCM), artificial synapses, hopfield neural network, stochastic process, optimization. I. INTRODUCTION O PTIMIZATION problems are among the most intensive computing tasks for several application fields, such as industry, finance, and transport. In general, optimization is carried out by several iterations to identify the global minimum of a certain cost function. In each iteration, a conventional digital system must access the memory to fetch input data and upload the temporary output, which is time and energy consuming. To enable a more efficient optimization, a non-von Neumann architecture can be adopted to eliminate the latency and energy spent for shuttling the data between the memory and the central processing unit (CPU) [1]. An example of non-von Neumann computing architecture is the concept of in-memory computing (IMC) where the computation is executed directly within the memory array. For instance, IMC can efficiently accelerate the typical multiplyaccumulate (MAC) operation, which is the foundation for modern digital accelerators for artificial intelligence (AI) and optimization [2]. Emerging memory devices, such as phase-change memory (PCM) [3], [4] and resistive random access memory (RRAM) [5], [6], offer scalable, efficient, and CMOS-compatible solutions to store analog information as

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Spiking analog VLSI neuron assemblies as constraint satisfaction problem solvers

Cited by 19 publications

References 11 publications

Advancing Neuromorphic Computing With Loihi: A Survey of Results and Outlook

Advancing Neuromorphic Computing With Loihi: A Survey of Results and Outlook

To Spike or Not to Spike: That Is the Question

A Spiking Recurrent Neural Network With Phase-Change Memory Neurons and Synapses for the Accelerated Solution of Constraint Satisfaction Problems

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