Energy-efficient model inference in wireless sensing: Asymmetric data processing

Author(s):  
Paul G Flikkema
2021 ◽  
pp. 1-1
Author(s):  
Yuxi Jiang ◽  
Yinghong Shuai ◽  
Xiaoliang He ◽  
Xing Wen ◽  
Liangliang Lou

Author(s):  
Sebastian Götz ◽  
Thomas Ilsche ◽  
Jorge Cardoso ◽  
Josef Spillner ◽  
Uwe ASSmann ◽  
...  

Author(s):  
Zhi Qiao ◽  
Shuwen Liang ◽  
Nandini Damera ◽  
Song Fu ◽  
Hsing-bung Chen ◽  
...  

2018 ◽  
Vol 28 (7) ◽  
pp. 1-6 ◽  
Author(s):  
Nikolay V. Klenov ◽  
Andrey E. Schegolev ◽  
Igor I. Soloviev ◽  
Sergey V. Bakurskiy ◽  
Maxim V. Tereshonok

2011 ◽  
Vol 11 (11) ◽  
pp. 2698-2710 ◽  
Author(s):  
Daniel Brenk ◽  
Jochen Essel ◽  
Juergen Heidrich ◽  
Roman Agethen ◽  
Dietmar Kissinger ◽  
...  

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2115
Author(s):  
Aleksandr Cariow ◽  
Janusz P. Paplinski

In this article, we propose a set of efficient algorithmic solutions for computing short linear convolutions focused on hardware implementation in VLSI. We consider convolutions for sequences of length N= 2, 3, 4, 5, 6, 7, and 8. Hardwired units that implement these algorithms can be used as building blocks when designing VLSI -based accelerators for more complex data processing systems. The proposed algorithms are focused on fully parallel hardware implementation, but compared to the naive approach to fully parallel hardware implementation, they require from 25% to about 60% less, depending on the length N and hardware multipliers. Since the multiplier takes up a much larger area on the chip than the adder and consumes more power, the proposed algorithms are resource-efficient and energy-efficient in terms of their hardware implementation.


2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Daejin Park ◽  
Jonghee M. Youn ◽  
Jeonghun Cho

A specially designed microcontroller with event-driven sensor data processing unit (EPU) is proposed to provide energy-efficient sensor data acquisition for Internet of Things (IoT) devices in rare-event human activity sensing applications. Rare-event sensing applications using a remotely installed IoT sensor device have a property of very long event-to-event distance, so that the inaccurate sensor data processing in a certain range of accuracy error is enough to extract appropriate events from the collected sensing data. The proposed signal-to-event converter (S2E) as a preprocessor of the conventional sensor interface extracts a set of atomic events with the specific features of interest and performs an early evaluation for the featured points of the incoming sensor signal. The conventional sensor data processing such as DSPs or software-driven algorithm to classify the meaningful event from the collected sensor data could be accomplished by the proposed event processing unit (EPU). The proposed microcontroller architecture enables an energy efficient signal processing for rare-event sensing applications. The implemented system-on-chip (SoC) including the proposed building blocks is fabricated with additional 7500 NAND gates and 1-KB SRAM tracer in 0.18 um CMOS process, consuming only 20% compared to the conventional sensor data processing method for human hand-gesture detection.


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