Aging-aware Lifetime Enhancement for Memristor-based Neuromorphic Computing

Zhang, Shuhang; Zhang, Grace Li; Bing, Li; Li, Hai; Schlichtmann, Ulf

doi:10.23919/date.2019.8714954

Cited by 45 publications

(22 citation statements)

References 15 publications

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“…In [174], Zhang et al propose an algorithm-software co-optimization for mapping vector-matrix computations in deep neural networks to mitigate limited endurance of memristors in a neuromorphic hardware. Authors show that during execution of deep learning models on the hardware, memristors need to be repeatedly tuned, i.e., reprogrammed using a pulse of very high voltage.…”

Section: System Software For Thermal and Reliability Optimizationmentioning

confidence: 99%

Implementing Spiking Neural Networks on Neuromorphic Architectures: A Review

Huynh¹,

Varshika²,

Paul³

et al. 2022

Preprint

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Recently, both industry and academia have proposed several different neuromorphic systems to execute machine learning applications that are designed using Spiking Neural Networks (SNNs). With the growing complexity on design and technology fronts, programming such systems to admit and execute a machine learning application is becoming increasingly challenging. Additionally, neuromorphic systems are required to guarantee real-time performance, consume lower energy, and provide tolerance to logic and memory failures. Consequently, there is a clear need for system software frameworks that can implement machine learning applications on current and emerging neuromorphic systems, and simultaneously address performance, energy, and reliability. Here, we provide a comprehensive overview of such frameworks proposed for both, platform-based design and hardware-software co-design. We highlight challenges and opportunities that the future holds in the area of system software technology for neuromorphic computing.

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Section: System Software For Thermal and Reliability Optimizationmentioning

confidence: 99%

Implementing Spiking Neural Networks on Neuromorphic Architectures: A Review

Huynh¹,

Varshika²,

Paul³

et al. 2022

Preprint

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“…There are other thermal-related reliability issues in memristors, for instance retention-time [65]- [68] and transistor circuit aging [24]- [26], [69]- [74]. Retention time is defined as the time for which a memristor can retain its programmed state.…”

Section: Other Reliability Issuesmentioning

confidence: 99%

Endurance-Aware Mapping of Spiking Neural Networks to Neuromorphic Hardware

Titirsha¹,

Song²,

Das³

et al. 2021

Preprint

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Neuromorphic computing systems are embracing memristors to implement high density and low power synaptic storage as crossbar arrays in hardware. These systems are energy efficient in executing Spiking Neural Networks (SNNs). We observe that long bitlines and wordlines in a memristive crossbar are a major source of parasitic voltage drops, which create current asymmetry. Through circuit simulations, we show the significant endurance variation that results from this asymmetry. Therefore, if the critical memristors (ones with lower endurance) are overutilized, they may lead to a reduction of the crossbar's lifetime. We propose eSpine, a novel technique to improve lifetime by incorporating the endurance variation within each crossbar in mapping machine learning workloads, ensuring that synapses with higher activation are always implemented on memristors with higher endurance, and vice versa. eSpine works in two steps. First, it uses the Kernighan-Lin Graph Partitioning algorithm to partition a workload into clusters of neurons and synapses, where each cluster can fit in a crossbar. Second, it uses an instance of Particle Swarm Optimization (PSO) to map clusters to tiles, where the placement of synapses of a cluster to memristors of a crossbar is performed by analyzing their activation within the workload. We evaluate eSpine for a state-of-the-art neuromorphic hardware model with phase-change memory (PCM)-based memristors. Using 10 SNN workloads, we demonstrate a significant improvement in the effective lifetime.

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“…Deep neural networks have achieved remarkable breakthroughs in various classication tasks in recent years. To accelerate the operations in such networks, various hardware platforms with emerging devices, e.g, RRAM/memristor [1][2][3][4][5][6], optical device [7][8][9], spintronics device [10,11] and Ferroelectric Field-Eect Transistor [12][13][14][15] have been proposed. One such promising platform is optical neural networks (ONNs).…”

Section: Introductionmentioning

confidence: 99%