8-bit Precision In-Memory Multiplication with Projected Phase-Change Memory

Giannopoulos, Iason; Sebastian, Abu; Gallo, Manuel Le; Jonnalagadda, Vara Prasad; Sousa, Marilyne; Boon, Marcus Nicolaas; Eleftheriou, Evangelos

doi:10.1109/iedm.2018.8614558

Cited by 69 publications

(41 citation statements)

References 9 publications

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“…The one-to-one comparison between the devices fabricated with our GST225 and GST467 films here ( Fig. 4d and e) shows that GST467 device exhibits a much-enhanced switching contrast resulting in up to 50% more in the number of interval states, important for photonic memory and neuromorphic devices 32,33 .…”

Section: Resultsmentioning

confidence: 90%

On-the-fly closed-loop materials discovery via Bayesian active learning

et al. 2020

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Active learning—the field of machine learning (ML) dedicated to optimal experiment design—has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics. In this work, we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property landscape. We demonstrate an autonomous materials discovery methodology for functional inorganic compounds which allow scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools. This robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs. The real-time closed-loop, autonomous system for materials exploration and optimization (CAMEO) is implemented at the synchrotron beamline to accelerate the interconnected tasks of phase mapping and property optimization, with each cycle taking seconds to minutes. We also demonstrate an embodiment of human-machine interaction, where human-in-the-loop is called to play a contributing role within each cycle. This work has resulted in the discovery of a novel epitaxial nanocomposite phase-change memory material.

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Section: Resultsmentioning

confidence: 90%

On-the-fly closed-loop materials discovery via Bayesian active learning

et al. 2020

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“…Also, it is essential to reduce the conductance drift to further improve the inference performance of SNN using PCM synapses. A projected-PCM cell architecture proposed recently demonstrate an order of magnitude lower drift coefficient and is a promising step in this direction 50,51 .…”

Section: Effects Of Non-ideal Synapsementioning

confidence: 99%

Experimental Demonstration of Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses

Nandakumar

Boybat

Gallo

et al. 2020

Sci Rep

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Spiking neural networks (Snn) are computational models inspired by the brain's ability to naturally encode and process information in the time domain. the added temporal dimension is believed to render them more computationally efficient than the conventional artificial neural networks, though their full computational capabilities are yet to be explored. Recently, in-memory computing architectures based on non-volatile memory crossbar arrays have shown great promise to implement parallel computations in artificial and spiking neural networks. In this work, we evaluate the feasibility to realize high-performance event-driven in-situ supervised learning systems using nanoscale and stochastic analog memory synapses. For the first time, the potential of analog memory synapses to generate precisely timed spikes in Snns is experimentally demonstrated. the experiment targets applications which directly integrates spike encoded signals generated from bio-mimetic sensors with in-memory computing based learning systems to generate precisely timed control signal spikes for neuromorphic actuators. More than 170,000 phase-change memory (PCM) based synapses from our prototype chip were trained based on an event-driven learning rule, to generate spike patterns with more than 85% of the spikes within a 25 ms tolerance interval in a 1250 ms long spike pattern. We observe that the accuracy is mainly limited by the imprecision related to device programming and temporal drift of conductance values. We show that an array level scaling scheme can significantly improve the retention of the trained Snn states in the presence of conductance drift in the pcM. combining the computational potential of supervised Snns with the parallel compute power of inmemory computing, this work paves the way for next-generation of efficient brain-inspired systems.

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“…The inference performance of SNN using PCM synapses could be further improved by reducing the inherent conductance drift from the devices. The recently demonstrated projected-PCM cell architecture with an order of magnitude lower drift coefficient is a promising step in this direction 46,47 .…”

Section: E On-chip Inferencementioning

confidence: 99%

Supervised learning in spiking neural networks with MLC PCM synapses

Nandakumar

Boybat

Gallo

et al. 2017

2017 75th Annual Device Research Conference (DRC)

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Spiking neural networks (SNN) are artificial computational models that have been inspired by the brain's ability to naturally encode and process information in the time domain. The added temporal dimension is believed to render them more computationally efficient than the conventional artificial neural networks, though their full computational capabilities are yet to be explored. Recently, computational memory architectures based on non-volatile memory crossbar arrays have shown great promise to implement parallel computations in artificial and spiking neural networks. In this work, we experimentally demonstrate for the first time, the feasibility to realize high-performance event-driven in-situ supervised learning systems using nanoscale and stochastic phase-change synapses. Our SNN is trained to recognize audio signals of alphabets encoded using spikes in the time domain and to generate spike trains at precise time instances to represent the pixel intensities of their corresponding images. Moreover, with a statistical model capturing the experimental behavior of the devices, we investigate architectural and systems-level solutions for improving the training and inference performance of our computational memory-based system. Combining the computational potential of supervised SNNs with the parallel compute power of computational memory, the work paves the way for next-generation of efficient brain-inspired systems.In recent years, deep learning algorithms have become successful in solving complex cognitive tasks surpassing the performance achievable by traditional algorithmic approaches, and in some cases, even expert humans. However, conventional computing architectures are confronted with several challenges while implementing the multi-layered artificial neural networks (ANNs) used in these algorithms, especially when compared against the approximately 20 W power budget of the human brain. The inefficiencies in the von Neumann architecture for neural network implementation arise from the high-precision digital representation of the network parameters, constant shuttling of large amounts of data between processor and memory, and the ensuing limited computational parallelism and scalability. In contrast, the human brain employs billions of neurons that communicate with each other in a parallel fashion, through dedicated, analog, and low-precision synaptic connections. The spike-based data encoding schemes used in these biological networks render the computation and communication asynchronous, event-driven, and sparse, contributing to the high computational efficiency of the brain.The size and complexity of artificial neural networks are expected to continue to grow in the future and has motivated the search for efficient and scalable hadware implementation schemes for learning systems 1 . Spiking neural networks (SNNs) are excellent candidates to implement large learning networks, especially for energy and memory-constrained embedded applications, as they closely mimic some of the key computational principles of...

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8-bit Precision In-Memory Multiplication with Projected Phase-Change Memory

Cited by 69 publications

References 9 publications

On-the-fly closed-loop materials discovery via Bayesian active learning

On-the-fly closed-loop materials discovery via Bayesian active learning

Experimental Demonstration of Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses

Supervised learning in spiking neural networks with MLC PCM synapses

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