Zobrazeno 1 - 10
of 249
pro vyhledávání: '"Ganguly, Udayan"'
Publikováno v:
Published 6 September 2023 IOP Publishing Ltd
Training deep neural networks (DNNs) is computationally intensive but arrays of non-volatile memories like Charge Trap Flash (CTF) can accelerate DNN operations using in-memory computing. Specifically, the Resistive Processing Unit (RPU) architecture
Externí odkaz:
http://arxiv.org/abs/2307.06088
Autor:
Meihar, Paritosh, Srinu, Rowtu, Lashkare, Sandip, Singh, Ajay Kumar, Mulaosmanovic, Halid, Deshpande, Veeresh, Dünkel, Stefan, Beyer, Sven, Ganguly, Udayan
In-memory computing on a reconfigurable architecture is the emerging field which performs an application-based resource allocation for computational efficiency and energy optimization. In this work, we propose a Ferroelectric MirrorBit-integrated fie
Externí odkaz:
http://arxiv.org/abs/2307.04705
Autor:
Patil, Shubham, Sharma, Anand, R, Gaurav, Kadam, Abhishek, Singh, Ajay Kumar, Lashkare, Sandip, Mohapatra, Nihar Ranjan, Ganguly, Udayan
Compact and energy-efficient Synapse and Neurons are essential to realize the full potential of neuromorphic computing. In addition, a low variability is indeed needed for neurons in Deep neural networks for higher accuracy. Further, process (P), vol
Externí odkaz:
http://arxiv.org/abs/2306.11640
Autor:
Ali, Md Hanif, Pandey, Adityanarayan, Srinu, Rowtu, Meihar, Paritosh, Patil, Shubham, Lashkare, Sandip, Ganguly, Udayan
Ferroelectricity in sputtered undoped-HfO$_2$ is attractive for composition control for low power and non-volatile memory and logic applications. Unlike doped HfO$_2$, evolution of ferroelectricity with annealing and film thickness effect in sputter
Externí odkaz:
http://arxiv.org/abs/2304.12924
Autor:
Patil, Shubham, Sakhuja, Jayatika, Singh, Ajay Kumar, Biswas, Anmol, Saraswat, Vivek, Kumar, Sandeep, Lashkare, Sandip, Ganguly, Udayan
Energy-efficient real-time synapses and neurons are essential to enable large-scale neuromorphic computing. In this paper, we propose and demonstrate the Schottky-Barrier MOSFET-based ultra-low power voltage-controlled current source to enable real-t
Externí odkaz:
http://arxiv.org/abs/2304.08504
Autor:
Meihar, Paritosh, Srinu, Rowtu, Saraswat, Vivek, Lashkare, Sandip, Mulaosmanovic, Halid, Singh, Ajay Kumar, Dünkel, Stefan, Beyer, Sven, Ganguly, Udayan
HfO2-based Ferroelectric field-effect transistor (FeFET) has become a center of attraction for non-volatile memory applications because of their low power, fast switching speed, high scalability, and CMOS compatibility. In this work, we show an n-cha
Externí odkaz:
http://arxiv.org/abs/2304.03124
Autor:
Rowtu, Srinu, Meihar, Paritosh, Pandey, Adityanarayan, Ali, Md. Hanif, Lashkare, Sandip, Ganguly, Udayan
In this work, we report a high remnant polarization, 2Pr >70$\mu$C/cm$^2$ in thermally processed atomic layer deposited Hf0.5Zr0.5O2 (HZO) film on Silicon with NH3 plasma exposed thin TiN interlayer and Tungsten (W) as a top electrode. The effect of
Externí odkaz:
http://arxiv.org/abs/2212.05026
Spiking Neural Networks (SNNs) have emerged as a hardware efficient architecture for classification tasks. The challenge of spike-based encoding has been the lack of a universal training mechanism performed entirely using spikes. There have been seve
Externí odkaz:
http://arxiv.org/abs/2207.09755
Autor:
Saraswat, Vivek, Ganguly, Udayan
Emerging non-volatile memories have been proposed for a wide range of applications from easing the von-Neumann bottleneck to neuromorphic applications. Specifically, scalable RRAMs based on Pr$_{1-x}$Ca$_x$MnO$_3$ (PCMO) exhibit analog switching have
Externí odkaz:
http://arxiv.org/abs/2111.02885
In sub-10nm FinFETs, Line-edge-roughness (LER) and metal-gate granularity (MGG) are the two most dominant sources of variability and are mostly modeled semi-empirically. In this work, compact models of LER and MGG are used. We show an accurate proces
Externí odkaz:
http://arxiv.org/abs/2109.00849