MemIntelli: A Generic End-to-End Simulation Framework for Memristive Intelligent Computing
Houji Zhou, Ling Yang, Zhiwei Zhou, Yi Li, Xiangshui Miao

TL;DR
MemIntelli is a comprehensive simulation framework that enables pre-verification of memristive in-memory computing systems across device, circuit, and application levels, supporting flexible precision and seamless integration with popular AI tools.
Contribution
It introduces the first end-to-end simulation framework for memristive IMC, supporting variable-precision computing and application-level validation.
Findings
Supports diverse intelligent algorithms including neural networks and data clustering
Enables co-design of memristive IMC systems from device to application
Compatible with NumPy and PyTorch for easy integration
Abstract
Memristive in-memory computing (IMC) has emerged as a promising solution for addressing the bottleneck in the Von Neumann architecture. However, the couplingbetweenthecircuitandalgorithm in IMC makes computing reliability susceptible to non-ideal effects in devices and peripheral circuits. In this respect, efficient softwarehardwareco-simulationtoolsarehighlydesiredtoembedthedevice and circuit models into the algorithms. In this paper, for the first time, we proposed an end-to-end simulation framework supporting flexible variable-precision computing, named MemIntelli, to realize the pre-verification of diverse intelligent applications on memristive devices. At the device and circuit level, mathematical functions are employed to abstract the devices and circuits through meticulous equivalent circuit modeling. On the architecture level, MemIntelli achieves flexible variable-precision IMC…
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Taxonomy
TopicsAdvanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices · Parallel Computing and Optimization Techniques
