Computing-in-Memory(CIM)architectures have emerged as a pivotal technology for nextgeneration artificial intelligence(AI)and edge computing applications.By enabling computations directly within memory cells,CIM archit...Computing-in-Memory(CIM)architectures have emerged as a pivotal technology for nextgeneration artificial intelligence(AI)and edge computing applications.By enabling computations directly within memory cells,CIM architectures effectively minimize data movement and significantly enhance energy efficiency.In the CIM system,the analog-to-digital converter(ADC)bridges the gap between efficient analog computation and general digital processing,while influencing the overall accuracy,speed and energy efficiency of the system.This review presents theoretical analyses and practical case studies on the performance requirements of ADCs and their optimization methods in CIM systems,aiming to provide ideas and references for the design and optimization of CIM systems.The review comprehensively explores the relationship between the design of CIM architectures and ADC optimization,and raises the issue of design trade-offs between low power consumption,high speed operation and compact integration design.On this basis,novel customized ADC optimization methods are discussed in depth,and a large number of current CIM systems and their ADC optimization examples are reviewed,with optimization methods summarized and classified in terms of power consumption,speed,and area.In the final part,this review analyzes energy efficiency,ENOB,and frequency scaling trends,demonstrating how advanced processes enable ADCs to balance speed,power,and area trade-offs,guiding ADC optimization for next-gen CIM systems.展开更多
Proteases are enzymes that cleave and hydrolyse the peptide bonds between two specific amino acid residues of target substrate proteins.Protease-controlled proteolysis plays a key role in the degradation and recycling...Proteases are enzymes that cleave and hydrolyse the peptide bonds between two specific amino acid residues of target substrate proteins.Protease-controlled proteolysis plays a key role in the degradation and recycling of proteins,which is essential for various physiological processes.Thus,solving the substrate identification problem will have important implications for the precise understanding of functions and physiological roles of proteases,as well as for therapeutic target identification and pharmaceutical applicability.Consequently,there is a great demand for bioinformatics methods that can predict novel substrate cleavage events with high accuracy by utilizing both sequence and structural information.In this study,we present Procleave,a novel bioinformatics approach for predicting protease-specific substrates and specific cleavage sites by taking into account both their sequence and 3D structural information.Structural features of known cleavage sites were represented by discrete values using a LOWESS data-smoothing optimization method,which turned out to be critical for the performance of Procleave.The optimal approximations of all structural parameter values were encoded in a conditional random field(CRF)computational framework,alongside sequence and chemical group-based features.Here,we demonstrate the outstanding performance of Procleave through extensive benchmarking and independent tests.Procleave is capable of correctly identifying most cleavage sites in the case study.Importantly,when applied to the human structural proteome encompassing 17,628 protein structures,Procleave suggests a number of potential novel target substrates and their corresponding cleavage sites of different proteases.Procleave is implemented as a webserver and is freely accessible at http://procleave.erc.monash.edu/.展开更多
基金the Zhejiang Provincial Natural Science Foundation of China under Grant LQN25F040002the Proof of Concept Foundation of Xidian University Hangzhou Institute of Technology under Grant GNYZ2024JC004,the National Key Research and the Postdoctoral Fellowship Program of CPSF under Grant GZC20241305.
文摘Computing-in-Memory(CIM)architectures have emerged as a pivotal technology for nextgeneration artificial intelligence(AI)and edge computing applications.By enabling computations directly within memory cells,CIM architectures effectively minimize data movement and significantly enhance energy efficiency.In the CIM system,the analog-to-digital converter(ADC)bridges the gap between efficient analog computation and general digital processing,while influencing the overall accuracy,speed and energy efficiency of the system.This review presents theoretical analyses and practical case studies on the performance requirements of ADCs and their optimization methods in CIM systems,aiming to provide ideas and references for the design and optimization of CIM systems.The review comprehensively explores the relationship between the design of CIM architectures and ADC optimization,and raises the issue of design trade-offs between low power consumption,high speed operation and compact integration design.On this basis,novel customized ADC optimization methods are discussed in depth,and a large number of current CIM systems and their ADC optimization examples are reviewed,with optimization methods summarized and classified in terms of power consumption,speed,and area.In the final part,this review analyzes energy efficiency,ENOB,and frequency scaling trends,demonstrating how advanced processes enable ADCs to balance speed,power,and area trade-offs,guiding ADC optimization for next-gen CIM systems.
基金financially supported by grants from the Australian Research Council(ARC)(Grant Nos.LP110200333 and DP120104460)National Health and Medical Research Council of Australia(NHMRC)(Grant Nos.APP1127948,APP1144652,and APP490989)+2 种基金the National Institute of Allergy and Infectious Diseases of the National Institutes of Health,USA(Grant No.R01 AI111965)a Major Inter-Disciplinary Research(IDR)Grant Awarded by Monash University,Australia(Grant Nos.2019-32 and 2018-28)supported in part by Informatics start-up packages through the School of Medicine,University of Alabama at Birmingham,USA
文摘Proteases are enzymes that cleave and hydrolyse the peptide bonds between two specific amino acid residues of target substrate proteins.Protease-controlled proteolysis plays a key role in the degradation and recycling of proteins,which is essential for various physiological processes.Thus,solving the substrate identification problem will have important implications for the precise understanding of functions and physiological roles of proteases,as well as for therapeutic target identification and pharmaceutical applicability.Consequently,there is a great demand for bioinformatics methods that can predict novel substrate cleavage events with high accuracy by utilizing both sequence and structural information.In this study,we present Procleave,a novel bioinformatics approach for predicting protease-specific substrates and specific cleavage sites by taking into account both their sequence and 3D structural information.Structural features of known cleavage sites were represented by discrete values using a LOWESS data-smoothing optimization method,which turned out to be critical for the performance of Procleave.The optimal approximations of all structural parameter values were encoded in a conditional random field(CRF)computational framework,alongside sequence and chemical group-based features.Here,we demonstrate the outstanding performance of Procleave through extensive benchmarking and independent tests.Procleave is capable of correctly identifying most cleavage sites in the case study.Importantly,when applied to the human structural proteome encompassing 17,628 protein structures,Procleave suggests a number of potential novel target substrates and their corresponding cleavage sites of different proteases.Procleave is implemented as a webserver and is freely accessible at http://procleave.erc.monash.edu/.
