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20182022
most citedCompiling Spiking Neural Networks to Neuromorphic Hardware

47 citations · 157 across the 23 of their papers we have counts for

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7 papers · 1 filter

cs.AR2022

Real-Time Scheduling of Machine Learning Operations on Heterogeneous Neuromorphic SoC

Anup Das

Neuromorphic Systems-on-Chip (NSoCs) are becoming heterogeneous by integrating general-purpose processors (GPPs) and neural processing units (NPUs) on the same SoC. For embedded sy…

cs.AR20214 cited

Special Session: Reliability Analysis for ML/AI Hardware

Shamik Kundu, Kanad Basu, Mehdi Sadi +4

Artificial intelligence (AI) and Machine Learning (ML) are becoming pervasive in today's applications, such as autonomous vehicles, healthcare, aerospace, cybersecurity, and many c…

cs.AR202016 cited

Aging-Aware Request Scheduling for Non-Volatile Main Memory

Shihao Song, Anup Das, Onur Mutlu +1

Modern computing systems are embracing non-volatile memory (NVM) to implement high-capacity and low-cost main memory. Elevated operating voltages of NVM accelerate the aging of CMO…

cs.AR20201 cited

Design Methodologies for Reliable and Energy-efficient PCM Systems

Shihao Song, Anup Das

Phase-change memory (PCM) is a scalable and low latency non-volatile memory (NVM) technology that has been proposed to serve as storage class memory (SCM), providing low access lat…

cs.AR202040 cited

Improving Phase Change Memory Performance with Data Content Aware Access

Shihao Song, Anup Das, Onur Mutlu +1

A prominent characteristic of write operation in Phase-Change Memory (PCM) is that its latency and energy are sensitive to the data to be written as well as the content that is ove…

cs.AR202016 cited

Exploiting Inter- and Intra-Memory Asymmetries for Data Mapping in Hybrid Tiered-Memories

Shihao Song, Anup Das, Nagarajan Kandasamy

Modern computing systems are embracing hybrid memory comprising of DRAM and non-volatile memory (NVM) to combine the best properties of both memory technologies, achieving low late…