[paper] Electrical characterization of advanced MOSFETs

Valeriya Kilchytska, Sergej Makovejev, Babak Kazemi Esfeh, Lucas Nyssens, Arka Halder,

Jean-Pierre Raskin and Denis Flandre

Electrical characterization of advanced MOSFETs towards analog and RF applications

IEEE LAEDC, San Jose, Costa Rica, 2020, 

doi: 10.1109/LAEDC49063.2020.9073536

Abstract - This invited paper reviews main approaches in the electrical characterization of advanced MOSFETs towards their target analog and RF applications. Advantages and necessity of those techniques will be demonstrated on different study cases of various advanced MOSFETs, such as FDSOI, FinFET, NW in a wide temperature range, based on our original research over the last years. 

URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9073536&isnumber=9072949

Acknowledgements - This work was partially funded by Eniac “Places2Be”, Ecsel “Waytogofast”, FNRS - FRFC “Towards Highly-efficient 10 nm MOSFETs”, FP7 “Nanosil” and “Nanofunction” projects. The authors thank our colleagues from CEA-Leti, ST and Imec, and particularly, F. Andrieu, O. Faynot, T. Poiroux, S. Barraud, M. Haond, N. Planes, N. Collaert, C. Claeys, M. Jurczak, B. Parvais, R. Rooyackers, for providing UTBB FD SOI, NW and FinFET devices and valuable discussions.

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[paper] Computing-in-Memory

Computing-in-Memory for Performance and Energy Efficient Homomorphic Encryption

Dayane Reis, Student Member, IEEE, Jonathan Takeshita, Taeho Jung, Member, IEEE, Michael Niemier, Senior Member, IEEE and Xiaobo Sharon Hu, Fellow, IEEE

preprint arXiv:2005.03002 (2020).

Abstract - Homomorphic encryption (HE) allows direct computations on encrypted data. Despite numerous research efforts, the practicality of HE schemes remains to be demonstrated. In this regard, the enormous size of ciphertexts involved in HE computations degrades computational efficiency. Near-memory Processing (NMP) and Computing-in-memory (CiM) — paradigms where computation is done within the memory boundaries — represent architectural solutions for reducing latency and energy associated with data transfers in data-intensive applications such as HE. This paper introduces CiM-HE, a Computing-in-memory (CiM) architecture that can support operations for the B/FV scheme, a somewhat homomorphic encryption scheme for general computation. CiM-HE hardware consists of customized peripherals such as sense amplifiers, adders, bit-shifters, and sequencing circuits. The peripherals are based on CMOS technology, and could support computations with memory cells of different technologies. Circuit-level simulations are used to evaluate our CiM-HE framework assuming a 6T-SRAM memory. We compare our CiM-HE implementation against (i) two optimized CPU HE implementations, and (ii) an FPGA-based HE accelerator implementation.When compared to a CPU solution, CiM-HE obtains speedups between 4.6x and 9.1x, and energy savings between 266.4x and 532.8x for homomorphic multiplications (the most expensive HE operation). Also, a set of four end-toend tasks, i.e., mean, variance, linear regression, and inference are up to 1.1x, 7.7x, 7.1x, and 7.5x faster (and 301.1x, 404.6x, 532.3x, and 532.8x more energy efficient). Compared to CPUbased HE in a previous work, CiM-HE obtain 14.3x speed-up and >2600x energy savings. Finally,our design offers 2.2x speed-up with 88.1x energy savings compared to a state-of-the-art FPGAbased accelerator.

Fig: Log shifter implemented in CiM-HE.

This work was supported in part by ASCENT, one of six centers in JUMP, a Semiconductor Research Corporation (SRC) program sponsored by DARPA.

Corresponding author: Xiaobo Sharon Hu, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA. e-mail: .