Abstract
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.
Highlights
O PTIMIZATION problems are among the most intensive computing tasks for several application fields, such as industry, finance, and transport
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]
in-memory computing (IMC) can efficiently accelerate the typical multiply– accumulate (MAC) operation, which is the foundation for modern digital accelerators for artificial intelligence (AI) and optimization [2]
Summary
O PTIMIZATION problems are among the most intensive computing tasks for several application fields, such as industry, finance, and transport. Note that the solution of a CSP in a Hopfield RNN becomes increasingly difficult when the number of the local minima increases because the network state can be trapped within a local minimum [17] To circumvent this limitation, the stochastic computational annealing is generally adopted, where the external stimulation is suitably mixed with random noise to help the system escape from local minima [19], [20]. A. STOCHASTIC PCM CRYSTALLIZATION The neuron integration function was implemented by a PCM device, where each applied pulse causes a partial crystallization in the amorphous volume. Note that the PCM device offers the unique physical property of stochastic integration of Fig. 2, which would not be feasible in other types of memory device, such as resistive switching memory of magnetic spin-torque memory
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
