Search Results - "Lu, Shengshuo"

A low power software-defined-radio baseband processor for the Internet of Things by Yajing Chen, Shengshuo Lu, Hun-Seok Kim, Blaauw, David, Dreslinski, Ronald G., Mudge, Trevor

“…In this paper, we define a configurable Software Defined Radio (SDR) baseband processor design for the Internet of Things (IoT). We analyzed the fundamental…”
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1.32GHz high-throughput charge-recovery AES core with resistance to DPA attacks by Shengshuo Lu, Zhengya Zhang, Papaefthymiou, Marios

“…A 128-bit Advanced Encryption Standard (AES) core targeted for high-performance security applications is fabricated in a 65nm CMOS technology. A novel…”
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Secure and Energy-Efficient Processors by Lu, Shengshuo

“…Security has become an essential part of digital information storage and processing. Both high-end and low-end applications, such as data centers and Internet…”
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A programmable Galois Field processor for the Internet of Things by Chen, Yajing, Lu, Shengshuo, Fu, Cheng, Blaauw, David, Dreslinski, Ronald, Mudge, Trevor, Kim, Hun-Seok

“…This paper investigates the feasibility of a unified processor architecture to enable error coding flexibility and secure communication in low power Internet…”
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A 1.25pJ/bit 0.048mm2 AES core with DPA resistance for IoT devices by Shengshuo Lu, Zhengya Zhang, Papaefthymiou, Marios

“…An AES core designed for low-cost and energy-efficient IoT security applications is fabricated in a 65nm CMOS technology. A novel Dual-Rail Flush Logic (DRFL)…”
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A 5.5GHz 0.84TOPS/mm2 neural network engine with stream architecture and resonant clock mesh by Shengshuo Lu, Zhengya Zhang, Papaefthymiou, Marios

“…This paper presents an ultra-high-performance neural network engine fabricated in a 65nm CMOS technology. The 0.9mm 2 core relies on an energy-efficient…”
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Rethinking Numerical Representations for Deep Neural Networks by Hill, Parker, Zamirai, Babak, Lu, Shengshuo, Chao, Yu-Wei, Laurenzano, Michael, Samadi, Mehrzad, Papaefthymiou, Marios, Mahlke, Scott, Wenisch, Thomas, Deng, Jia, Tang, Lingjia, Mars, Jason

“…With ever-increasing computational demand for deep learning, it is critical to investigate the implications of the numeric representation and precision of DNN…”
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