Ferroelectric FET Based In-Memory Computing for Few-Shot Learning

Laguna, Ann Franchesca; Yin, Xunzhao; Reis, Dayane; Niemier, Michael; Hu, Xiaobo Sharon

doi:10.1145/3299874.3319450

Cited by 33 publications

(23 citation statements)

References 22 publications

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“…We propose simple and dimensionalitypreserving transformations to directly modify real-valued vectors to dense bipolar and dense binary vectors. This is in contrast to prior work 11,13,14 that involves additional quantization, mapping, and coding schemes. In the following, we describe how our systematic transition first transforms the real-valued HD vectors to bipolar.…”

Section: Resultsmentioning

confidence: 88%

“…In the MANN architectures, the key-value memory remains mostly independent of the task and input type, while the controller should be fitted to the task and especially the input type. Convolutional neural networks (CNNs) are excellent controllers for few-shot Omniglot 23 image classification task (see Methods) that has established itself as the core benchmark for the MANNs 6,11,13,14 . We have chosen a 5-layer CNN controller that provides an embedding function to map the input image to an internal feature representation (see Methods).…”

Section: Resultsmentioning

confidence: 99%

“…The experimental results for few-shot problems with varying complexities are presented. For the Omniglot dataset, a few problems have established themselves as standards such as 5-way and 20-way with 1-shot and 5-shots each 11,13,14,[28][29][30][31][32] . There has been no effort for scaling to more complex problems (i.e., more ways/shots) on the Omniglot dataset so far.…”

Section: Resultsmentioning

confidence: 99%

“…One promising alternative is to realize a key memory with nonvolatile memory (NVM) devices that can also serve as computational memory to efficiently execute such memory intensive operations 10,11 . Initial simulation results have suggested key memory architectures using NVM devices such as spintronic devices 10 , resistive random access memory (RRAM) 12 , and ferroelectric field-effect transistors (FeFETs) 13 . To map a vector component in the key memory, devices have either been simulated with high multibit precision 10 , or multiple ternary CAM (TCAM) cells by using intermediate mapping functions and encoding to get a binary code 13,14 .…”

mentioning

confidence: 99%

“…Initial simulation results have suggested key memory architectures using NVM devices such as spintronic devices 10 , resistive random access memory (RRAM) 12 , and ferroelectric field-effect transistors (FeFETs) 13 . To map a vector component in the key memory, devices have either been simulated with high multibit precision 10 , or multiple ternary CAM (TCAM) cells by using intermediate mapping functions and encoding to get a binary code 13,14 . Besides these simulation results, a recent prototype has demonstrated the use of a very small scale 2 × 2 TCAM array based on FeFETs 11 .…”

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confidence: 99%

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Robust high-dimensional memory-augmented neural networks

et al. 2021

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Traditional neural networks require enormous amounts of data to build their complex mappings during a slow training procedure that hinders their abilities for relearning and adapting to new data. Memory-augmented neural networks enhance neural networks with an explicit memory to overcome these issues. Access to this explicit memory, however, occurs via soft read and write operations involving every individual memory entry, resulting in a bottleneck when implemented using the conventional von Neumann computer architecture. To overcome this bottleneck, we propose a robust architecture that employs a computational memory unit as the explicit memory performing analog in-memory computation on high-dimensional (HD) vectors, while closely matching 32-bit software-equivalent accuracy. This is achieved by a content-based attention mechanism that represents unrelated items in the computational memory with uncorrelated HD vectors, whose real-valued components can be readily approximated by binary, or bipolar components. Experimental results demonstrate the efficacy of our approach on few-shot image classification tasks on the Omniglot dataset using more than 256,000 phase-change memory devices. Our approach effectively merges the richness of deep neural network representations with HD computing that paves the way for robust vector-symbolic manipulations applicable in reasoning, fusion, and compression.

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Section: Resultsmentioning

confidence: 88%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Robust high-dimensional memory-augmented neural networks

et al. 2021

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Experimentally validated memristive memory augmented neural network with efficient hashing and similarity search

et al. 2022

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Lifelong on-device learning is a key challenge for machine intelligence, and this requires learning from few, often single, samples. Memory-augmented neural networks have been proposed to achieve the goal, but the memory module must be stored in off-chip memory, heavily limiting the practical use. In this work, we experimentally validated that all different structures in the memory-augmented neural network can be implemented in a fully integrated memristive crossbar platform with an accuracy that closely matches digital hardware. The successful demonstration is supported by implementing new functions in crossbars, including the crossbar-based content-addressable memory and locality sensitive hashing exploiting the intrinsic stochasticity of memristor devices. Simulations show that such an implementation can be efficiently scaled up for one-shot learning on more complex tasks. The successful demonstration paves the way for practical on-device lifelong learning and opens possibilities for novel attention-based algorithms that were not possible in conventional hardware.

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Ferroelectric field-effect transistors based on HfO₂: a review

Mulaosmanovic¹,

Breyer²,

Dünkel³

et al. 2021

Nanotechnology

190

116

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In this article, we review the recent progress of ferroelectric field-effect transistors (FeFETs) based on ferroelectric hafnium oxide (HfO2), ten years after the first report on such a device. With a focus on the use of FeFET for nonvolatile memory application, we discuss its basic operation principles, switching mechanisms, device types, material properties and array structures. Key device performance metrics such as cycling endurance, retention, memory window, multi-level operation and scaling capability are analyzed. We also briefly survey recent developments in alternative applications for FeFETs including neuromorphic and in-memory computing as well as radiofrequency devices.

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Ferroelectric FET Based In-Memory Computing for Few-Shot Learning

Cited by 33 publications

References 22 publications

Robust high-dimensional memory-augmented neural networks

Robust high-dimensional memory-augmented neural networks

Experimentally validated memristive memory augmented neural network with efficient hashing and similarity search

Ferroelectric field-effect transistors based on HfO₂: a review

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Ferroelectric FET Based In-Memory Computing for Few-Shot Learning

Cited by 33 publications

References 22 publications

Robust high-dimensional memory-augmented neural networks

Robust high-dimensional memory-augmented neural networks

Experimentally validated memristive memory augmented neural network with efficient hashing and similarity search

Ferroelectric field-effect transistors based on HfO2: a review

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Ferroelectric field-effect transistors based on HfO₂: a review