scholarly journals On-chip Fano-like Resonator With a High Slope and Low-complexity Fabrication Processing

2022 ◽  
pp. 1-1
Author(s):  
Shasha Liao ◽  
Tiantian Zhang ◽  
Hang Bao ◽  
Yejun Liu ◽  
Li Liu
Keyword(s):  
Low Complexity ◽  
High Slope ◽  
On Chip

Low-Complexity Nonlinear Self-Inverse Permutation for Creating Physically Clone-Resistant Identities

Cryptography ◽  
2020 ◽  
Vol 4 (1) ◽  
pp. 6 ◽  
Author(s):  
Saleh Mulhem ◽  
Ayoub Mars ◽  
Wael Adi
Keyword(s):  
Field Programmable Gate Arrays ◽  
Low Complexity ◽  
System On Chip ◽  
Large Classes ◽  
Physical Unclonable Functions ◽  
Gate Arrays ◽  
Field Programmable ◽  
Programmable Gate Arrays ◽  
Security Levels ◽  
On Chip

New large classes of permutations over ℤ 2 n based on T-Functions as Self-Inverting Permutation Functions (SIPFs) are presented. The presented classes exhibit negligible or low complexity when implemented in emerging FPGA technologies. The target use of such functions is in creating the so called Secret Unknown Ciphers (SUC) to serve as resilient Clone-Resistant structures in smart non-volatile Field Programmable Gate Arrays (FPGA) devices. SUCs concepts were proposed a decade ago as digital consistent alternatives to the conventional analog inconsistent Physical Unclonable Functions PUFs. The proposed permutation classes are designed and optimized particularly to use non-consumed Mathblock cores in programmable System-on-Chip (SoC) FPGA devices. Hardware and software complexities for realizing such structures are optimized and evaluated for a sample expected target FPGA technology. The attained security levels of the resulting SUCs are evaluated and shown to be scalable and usable even for post-quantum crypto systems.

Hamming Product Code Based Multiple Bit Error Correction Coding Scheme Using Keyboard Scan Based Decoding for on Chip Interconnects Links

2012 ◽  
Vol 241-244 ◽  
pp. 2457-2461 ◽  
Author(s):  
Murali Maheswari ◽  
Gopalakrishnan Seetharaman
Keyword(s):  
Error Correction ◽  
Error Rate ◽  
Iterative Decoding ◽  
Error Control ◽  
Low Complexity ◽  
Hardware Complexity ◽  
Error Correction Coding ◽  
Product Code ◽  
Coding Scheme ◽  
On Chip

In this paper, we present multiple bit error correction coding scheme using extended Hamming product code combined with type II HARQ and keyboard scan based error flipping to correct multiple bit errors for on chip interconnect. The keyboard scan based error flipping reduces the hardware complexity of the decoder compared to the existing three stages iterative decoding method for on chip interconnects. The proposed method of decoding achieves 86% of reduction in area and 23% of reduction in decoder delay with only small increase in residual flit error rate compared to the existing three stage iterative decoding scheme for multiple bit error correction. The proposed code also achieves excellent improvement in residual flit error rate and up to 66% of links power consumption compared to the other error control schemes. The low complexity and excellent residual flit error rate make the proposed code suitable for on chip interconnection links.

scholarly journals Low Complexity System on Chip Design to Acquire Signals from MOS Gas Sensor Applications

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6552
Author(s):  
Juan B. Talens ◽  
Jose Pelegri-Sebastia ◽  
Maria Jose Canet
Keyword(s):  
Gas Sensor ◽  
Low Voltage ◽  
Low Cost ◽  
Low Complexity ◽  
System On Chip ◽  
Working Environment ◽  
Sigma Delta ◽  
Sensor Applications ◽  
Analog Signals ◽  
On Chip

Analog signals from gas sensors are used to recognize all types of VOC (Volatile Organic Compound) substances, such as toxic gases, tobacco or ethanol. The processes to recognize these substances include acquisition, treatment and machine learning for classification, which can all be efficiently implemented on a Field Programmable Gate Array (FPGA) aided by Low-Voltage Differential Signaling (LVDS). This article proposes a low-cost 11-bit effective number of bits (ENOB) sigma-delta Analog to Digital Converter (ADC), with an SNR of 75.97 dB and an SFDR of 72.28 dB, whose output is presented on screen in real time, thanks to the use of a Linux System on Chip (SoC) system that enables parallelism, high-level programming and provides a working environment for the scientific treatment of gas sensor signals. The high frequency achieved by the implemented ADC allows for multiplexing the capture of several analog signals with an optimal resolution. Additionally, several ADCs can be implemented in the same FPGA so several analog signals can be digitalized in parallel.

scholarly journals Low-Power Audio Keyword Spotting Using Tsetlin Machines

2021 ◽  
Vol 11 (2) ◽  
pp. 18
Author(s):  
Jie Lei ◽  
Tousif Rahman ◽  
Rishad Shafik ◽  
Adrian Wheeldon ◽  
Alex Yakovlev ◽  
...  
Keyword(s):  
Low Power ◽  
Learning Algorithm ◽  
Learning Automata ◽  
Low Complexity ◽  
Keyword Spotting ◽  
High Learning ◽  
Memory Footprint ◽  
Learning Efficacy ◽  
On Chip ◽  
Machine Interaction

The emergence of artificial intelligence (AI) driven keyword spotting (KWS) technologies has revolutionized human to machine interaction. Yet, the challenge of end-to-end energy efficiency, memory footprint and system complexity of current neural network (NN) powered AI-KWS pipelines has remained ever present. This paper evaluates KWS utilizing a learning automata powered machine learning algorithm called the Tsetlin Machine (TM). Through significant reduction in parameter requirements and choosing logic over arithmetic-based processing, the TM offers new opportunities for low-power KWS while maintaining high learning efficacy. In this paper, we explore a TM-based keyword spotting (KWS) pipeline to demonstrate low complexity with faster rate of convergence compared to NNs. Further, we investigate the scalability with increasing keywords and explore the potential for enabling low-power on-chip KWS.

scholarly journals Low-Power Audio Keyword Spotting using Tsetlin Machines

2021 ◽  
Author(s):  
Jie Lei ◽  
Tousif Rahman ◽  
Rishad Shafik ◽  
Alex Yakovlev ◽  
Alex Yakovlev ◽  
...  
Keyword(s):  
Low Power ◽  
Learning Algorithm ◽  
Learning Automata ◽  
Low Complexity ◽  
Keyword Spotting ◽  
High Learning ◽  
Memory Footprint ◽  
Learning Efficacy ◽  
On Chip ◽  
Machine Interaction

The emergence of Artificial Intelligence (AI) driven Keyword Spotting (KWS) technologies has revolutionized human to machine interaction. Yet, the challenge of end-to-end energy efficiency, memory footprint and system complexity of current Neural Network (NN) powered AI-KWS pipelines has remained ever present. This paper evaluates KWS utilizing a learning automata powered machine learning algorithm called the Tsetlin Machine (TM). Through significant reduction in parameter requirements and choosing logic over arithmetic based processing, the TM offers new opportunities for low-power KWS while maintaining high learning efficacy. In this paper we explore a TM based keyword spotting (KWS) pipeline to demonstrate low complexity with faster rate of convergence compared to NNs. Further, we investigate the scalability with increasing keywords and explore the potential for enabling low-power on-chip KWS.

Low-Complexity Link Microarchitecture for Mesochronous Communication in Networks-on-Chip

2008 ◽  
Vol 57 (9) ◽  
pp. 1196-1201 ◽  
Author(s):  
Francesco Vitullo ◽  
Nicola E. L'Insalata ◽  
Esa Petri ◽  
Sergio Saponara ◽  
Luca Fanucci ◽  
...  
Keyword(s):  
Low Complexity ◽  
Networks On Chip ◽  
On Chip
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