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Related publications (5)

Chaosity: Understanding Contemporary NUMA-architectures

Anastasia Ailamaki, Viktor Sanca, Syed Mohammad Aunn Raza, Hamish Mcniece Hill Nicholson, Andreea Nica

Modern hardware is increasingly complex, requiring increasing effort to understand in order to carefully engineer systems for optimal performance and effective utilization. Moreover, established design principles and assumptions are not portable to modern ...

2023

A 500 x 500 Dual-Gate SPAD Imager With 100% Temporal Aperture and 1 ns Minimum Gate Length for FLIM and Phasor Imaging Applications

Edoardo Charbon, Claudio Bruschini, Andrei Ardelean, Paul Mos, Arin Can Ülkü, Michael Alan Wayne

In this article, we report on SwissSPAD3 (SS3), a 500 x 500 pixel single-photon avalanche diode (SPAD) array, fabricated in 0.18-mu m CMOS technology. In this sensor, we introduce a novel dual-gate architecture with two contiguous temporal windows, or gate ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Reducing circuit design complexity for neuromorphic machine learning systems based on Non-Volatile Memory arrays

Yusuf Leblebici, Alessandro Fumarola

Machine Learning (ML) is an attractive application of Non-Volatile Memory (NVM) arrays [1,2]. However, achieving speedup over GPUs will require minimal neuron circuit sharing and thus highly area-efficient peripheral circuitry, so that ML reads and writes ...

2017

Neuromorphic Computing Using Non-Volatile Memory

Yusuf Leblebici, Irem Boybat Kara, Alessandro Fumarola, Seyoung Kim, Abu Sebastian, Severin Sidler

Dense crossbar arrays of non-volatile memory (NVM) devices represent one possible path for implementing massively-parallel and highly energy-efficient neuromorphic computing systems. We first review recent advances in the application of NVM devices to thre ...

2016

PCM for Neuromorphic Applications: Impact of Device Characteristics on Neural Network Performance

Yusuf Leblebici, Severin Sidler, Irem Boybat Kara, Carmelo Di Nolfo

The impact of Phase Change Memory (PCM) as well as other Non-Volatile Memory (NVM) device characteristics on the quantitative classification performance of artificial neural networks is studied. Our results show that any NVM-based neural network — not just ...

2015

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