Pub Date : 2023-10-26DOI: 10.1109/JETCAS.2023.3327748
Abhiroop Bhattacharjee;Abhishek Moitra;Priyadarshini Panda
Today, there are a plethora of In-Memory Computing (IMC) devices- SRAMs, PCMs & FeFETs, that emulate convolutions on crossbar-arrays with high throughput. Each IMC device offers its own pros & cons during inference of Deep Neural Networks (DNNs) on crossbars in terms of area overhead, programming energy and non-idealities. A design-space exploration is, therefore, imperative to derive a hybrid-device architecture optimized for accurate DNN inference under the impact of non-idealities from multiple devices, while maintaining competitive area & energy-efficiencies. We propose a two-phase search framework (HyDe) that exploits the best of all worlds offered by multiple devices to determine an optimal hybrid-device architecture for a given DNN topology. Our hybrid models achieve upto $2.30-2.74times $ higher $TOPS/mm^{2}$ at 22 – 26% higher energy-efficiencies than baseline homogeneous models for a VGG16 DNN topology. We further propose a feasible implementation of the HyDe-derived hybrid-device architectures in the 2.5D design space using chiplets to reduce design effort and cost in the hardware fabrication involving multiple technology processes.
{"title":"HyDe: A brid PCM/FeFET/SRAM vice-Search for Optimizing Area and Energy-Efficiencies in Analog IMC Platforms","authors":"Abhiroop Bhattacharjee;Abhishek Moitra;Priyadarshini Panda","doi":"10.1109/JETCAS.2023.3327748","DOIUrl":"10.1109/JETCAS.2023.3327748","url":null,"abstract":"Today, there are a plethora of In-Memory Computing (IMC) devices- SRAMs, PCMs & FeFETs, that emulate convolutions on crossbar-arrays with high throughput. Each IMC device offers its own pros & cons during inference of Deep Neural Networks (DNNs) on crossbars in terms of area overhead, programming energy and non-idealities. A design-space exploration is, therefore, imperative to derive a hybrid-device architecture optimized for accurate DNN inference under the impact of non-idealities from multiple devices, while maintaining competitive area & energy-efficiencies. We propose a two-phase search framework (HyDe) that exploits the best of all worlds offered by multiple devices to determine an optimal hybrid-device architecture for a given DNN topology. Our hybrid models achieve upto \u0000<inline-formula> <tex-math>$2.30-2.74times $ </tex-math></inline-formula>\u0000 higher \u0000<inline-formula> <tex-math>$TOPS/mm^{2}$ </tex-math></inline-formula>\u0000 at 22 – 26% higher energy-efficiencies than baseline homogeneous models for a VGG16 DNN topology. We further propose a feasible implementation of the HyDe-derived hybrid-device architectures in the 2.5D design space using chiplets to reduce design effort and cost in the hardware fabrication involving multiple technology processes.","PeriodicalId":48827,"journal":{"name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136159507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Neuromorphic computing based on spike neural networks (SNNs) exhibits great potential in reducing energy consumption in hardware systems. Resistive random-access memory (ReRAM) is regarded as a promising candidate to construct neuromorphic hardware, attributing to their high-density, nonvolatile, and compute-in-memory capability. However, the ReRAM-based neuromorphic chips are still in their infancy, cannot support multicore or with limited neuron configurability. To alleviate these problems, we propose a hybrid multicore SNN chip based on 60K-ReRAM synapses and 480-digital neurons in the 180 nm node, achieving a synaptic density of 20K bit/mm2 per core. To improve the efficiency of inter-core communication, we adopt a network-on-chip architecture with a bit character encoding strategy. In addition, an adaptive multiplier-less digital neuron is designed to support both Izhikevich and leaky integrate-and-fire models through register bit control, meeting different application scenarios. Finally, we evaluate the performance of our chip on the MNIST dataset recognition tasks, achieving 97.65% accuracy. Also, a minimum energy per synaptic operation (SOP) of 6.6 pJ in the 180 nm node is obtained, outperforming the TrueNorth’s 26 pJ in 28 nm. These results show that our design has a great potential for large-scale SNN implementations and may pave the way for designing high-efficient neuromorphic hardware with ReRAM technology.
{"title":"Multicore Spiking Neuromorphic Chip in 180-nm With ReRAM Synapses and Digital Neurons","authors":"Hao Jiang;Jikai Lu;Chenggao Zhang;Shuangzhu Tang;Junjie An;Lingli Cheng;Jian Lu;Jinsong Wei;Keji Zhou;Xumeng Zhang;Tuo Shi;Qi Liu","doi":"10.1109/JETCAS.2023.3325158","DOIUrl":"10.1109/JETCAS.2023.3325158","url":null,"abstract":"Neuromorphic computing based on spike neural networks (SNNs) exhibits great potential in reducing energy consumption in hardware systems. Resistive random-access memory (ReRAM) is regarded as a promising candidate to construct neuromorphic hardware, attributing to their high-density, nonvolatile, and compute-in-memory capability. However, the ReRAM-based neuromorphic chips are still in their infancy, cannot support multicore or with limited neuron configurability. To alleviate these problems, we propose a hybrid multicore SNN chip based on 60K-ReRAM synapses and 480-digital neurons in the 180 nm node, achieving a synaptic density of 20K bit/mm2 per core. To improve the efficiency of inter-core communication, we adopt a network-on-chip architecture with a bit character encoding strategy. In addition, an adaptive multiplier-less digital neuron is designed to support both Izhikevich and leaky integrate-and-fire models through register bit control, meeting different application scenarios. Finally, we evaluate the performance of our chip on the MNIST dataset recognition tasks, achieving 97.65% accuracy. Also, a minimum energy per synaptic operation (SOP) of 6.6 pJ in the 180 nm node is obtained, outperforming the TrueNorth’s 26 pJ in 28 nm. These results show that our design has a great potential for large-scale SNN implementations and may pave the way for designing high-efficient neuromorphic hardware with ReRAM technology.","PeriodicalId":48827,"journal":{"name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136366271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-04DOI: 10.1109/JETCAS.2023.3321771
Sangyeob Kim;Hoi-Jun Yoo
In this article, we propose a Complementary Deep-Neural-Network (C-DNN) processor V2 by optimizing the performance improvement from combination of CNN and SNN. C-DNN V1 showcased the potential for achieving higher energy efficiency by combining CNN and SNN. However, it encountered 5 challenges that hindered the full realization of this potential: Inefficiency of the clock gating accumulator, imbalance in spike sparsity across different time-steps, redundant cache power stemming from temporal weight reuse, limited performance of the SNN core for dense spike trains, and nonoptimal operation resulting from tile-based workload division. To overcome these challenges and achieve enhanced energy efficiency through the CNN-SNN combination, C-DNN V2 is developed. It addresses these challenges by implementing a Full-Adder/OR-based reduction tree, which reduces power consumption in the SNN core under high spike sparsity conditions. Additionally, it efficiently manages spike sparsity imbalances between dense and sparse SNN cores by integrating them simultaneously. The proposed reconfigurable spatial weight reuse method decreases the number of redundant register files and their power consumption. The spike flipping and inhibition method facilitate efficient processing of input data with high spike sparsity in the SNN core. Furthermore, fine-grained workload division and a high sparsity-aware CNN core are introduced to ensure optimal processing of each data in the domain with the highest energy efficiency. In conclusion, we propose the C-DNN V2 as an optimal complementary DNN processor, delivering 76.9% accuracy for ImageNet classification with a state-of-the-art energy efficiency of 32.8 TOPS/W.
{"title":"C-DNN V2: Complementary Deep-Neural-Network Processor With Full-Adder/OR-Based Reduction Tree and Reconfigurable Spatial Weight Reuse","authors":"Sangyeob Kim;Hoi-Jun Yoo","doi":"10.1109/JETCAS.2023.3321771","DOIUrl":"10.1109/JETCAS.2023.3321771","url":null,"abstract":"In this article, we propose a Complementary Deep-Neural-Network (C-DNN) processor V2 by optimizing the performance improvement from combination of CNN and SNN. C-DNN V1 showcased the potential for achieving higher energy efficiency by combining CNN and SNN. However, it encountered 5 challenges that hindered the full realization of this potential: Inefficiency of the clock gating accumulator, imbalance in spike sparsity across different time-steps, redundant cache power stemming from temporal weight reuse, limited performance of the SNN core for dense spike trains, and nonoptimal operation resulting from tile-based workload division. To overcome these challenges and achieve enhanced energy efficiency through the CNN-SNN combination, C-DNN V2 is developed. It addresses these challenges by implementing a Full-Adder/OR-based reduction tree, which reduces power consumption in the SNN core under high spike sparsity conditions. Additionally, it efficiently manages spike sparsity imbalances between dense and sparse SNN cores by integrating them simultaneously. The proposed reconfigurable spatial weight reuse method decreases the number of redundant register files and their power consumption. The spike flipping and inhibition method facilitate efficient processing of input data with high spike sparsity in the SNN core. Furthermore, fine-grained workload division and a high sparsity-aware CNN core are introduced to ensure optimal processing of each data in the domain with the highest energy efficiency. In conclusion, we propose the C-DNN V2 as an optimal complementary DNN processor, delivering 76.9% accuracy for ImageNet classification with a state-of-the-art energy efficiency of 32.8 TOPS/W.","PeriodicalId":48827,"journal":{"name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135955106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-02DOI: 10.1109/JETCAS.2023.3321105
Ben Varkey Benjamin;Richelle L. Smith;Kwabena A. Boahen
To convert a subthreshold current to a pulse frequency efficiently and predictably, we designed a silicon soma that conserves energy with current feedback and lessens thermal sensitivity with voltage feedback. When the input current charges a capacitor close to the inversion point of an inverter, its short-circuit current wastes energy. To shorten this period, existing designs accelerate the charging rate with positive feedback: Either a capacitive divider feeds back voltage or a current mirror feeds back current. Voltage feedback is less effective because it kicks in only at the inversion point. Current feedback is less predictable because its leakage current is exponentially sensitive to temperature variation. By quantifying this thermal sensitivity with an analytic model of the subthreshold MOS transistor, we successfully combined current feedback with voltage feedback to design a silicon soma 10-fold less sensitive to temperature than a previous current-feedback-only design that uses 7.6-fold more silicon area. This advance allowed a mixed-signal neuromorphic chip to be predictably programmed for the first time.
为了高效、可预测地将阈下电流转换为脉冲频率，我们设计了一种硅体，它通过电流反馈节省能量，通过电压反馈降低热敏感性。当输入电流对接近反相器反相点的电容器充电时，其短路电流会浪费能量。为了缩短这段时间，现有设计通过正反馈加快充电速度：要么是电容分压器反馈电压，要么是电流镜反馈电流。电压反馈的效果较差，因为它只在反相点起作用。电流反馈的可预测性较差，因为其泄漏电流对温度变化呈指数级敏感。通过使用亚阈值 MOS 晶体管的分析模型量化这种热敏感性，我们成功地将电流反馈与电压反馈结合起来，设计出了一种对温度敏感性比以前的纯电流反馈设计低 10 倍的硅 Soma，其硅面积比以前的设计大 7.6 倍。这一进步首次实现了混合信号神经形态芯片的可预测性编程。
{"title":"A Low Thermal Sensitivity Subthreshold-Current to Pulse-Frequency Converter for Neuromorphic Chips","authors":"Ben Varkey Benjamin;Richelle L. Smith;Kwabena A. Boahen","doi":"10.1109/JETCAS.2023.3321105","DOIUrl":"10.1109/JETCAS.2023.3321105","url":null,"abstract":"To convert a subthreshold current to a pulse frequency efficiently and predictably, we designed a silicon soma that conserves energy with current feedback and lessens thermal sensitivity with voltage feedback. When the input current charges a capacitor close to the inversion point of an inverter, its short-circuit current wastes energy. To shorten this period, existing designs accelerate the charging rate with positive feedback: Either a capacitive divider feeds back voltage or a current mirror feeds back current. Voltage feedback is less effective because it kicks in only at the inversion point. Current feedback is less predictable because its leakage current is exponentially sensitive to temperature variation. By quantifying this thermal sensitivity with an analytic model of the subthreshold MOS transistor, we successfully combined current feedback with voltage feedback to design a silicon soma 10-fold less sensitive to temperature than a previous current-feedback-only design that uses 7.6-fold more silicon area. This advance allowed a mixed-signal neuromorphic chip to be predictably programmed for the first time.","PeriodicalId":48827,"journal":{"name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135845360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Spiking neural network (SNN)-based compute-in-memory (CIM) accelerator provides a prospective implementation for intelligent edge devices with higher energy efficiency compared with artificial neural networks (ANN) deployed on conventional Von Neumann architectures. However, the costly circuit implementation of biological neurons and the immature training algorithm of discrete-pulse networks hinder efficient hardware implementation and high recognition rate. In this work, we present a 40nm RRAM CIM macro (Tempo-CIM) with charge-pump-based leaky-integrate-and-fire (LIF) neurons and split-train-merged-inference algorithm for efficient SNN acceleration with improved accuracy. The single-spike latency coding is employed to reduce the number of pulses in each time step. The voltage-type LIF neuron uses a charge pump structure to achieve efficient accumulation and thus reduce the requirement for large capacitance remarkably. The split-train-merged-inference algorithm is proposed to dynamically adjust the input of each neuron to alleviate the spike stall problem. The macro measures 0.084mm2 in a 40nm process with an energy efficiency of 68.51 TOPS/W and an area efficiency of 0.1956 TOPS/mm2 for 4b input and 8b weight.
{"title":"Tempo-CIM: A RRAM Compute-in-Memory Neuromorphic Accelerator With Area-Efficient LIF Neuron and Split-Train-Merged-Inference Algorithm for Edge AI Applications","authors":"Jingwen Jiang;Keji Zhou;Jinhao Liang;Fengshi Tian;Chenyang Zhao;Jianguo Yang;Xiaoyong Xue;Xiaoyang Zeng","doi":"10.1109/JETCAS.2023.3321107","DOIUrl":"10.1109/JETCAS.2023.3321107","url":null,"abstract":"Spiking neural network (SNN)-based compute-in-memory (CIM) accelerator provides a prospective implementation for intelligent edge devices with higher energy efficiency compared with artificial neural networks (ANN) deployed on conventional Von Neumann architectures. However, the costly circuit implementation of biological neurons and the immature training algorithm of discrete-pulse networks hinder efficient hardware implementation and high recognition rate. In this work, we present a 40nm RRAM CIM macro (Tempo-CIM) with charge-pump-based leaky-integrate-and-fire (LIF) neurons and split-train-merged-inference algorithm for efficient SNN acceleration with improved accuracy. The single-spike latency coding is employed to reduce the number of pulses in each time step. The voltage-type LIF neuron uses a charge pump structure to achieve efficient accumulation and thus reduce the requirement for large capacitance remarkably. The split-train-merged-inference algorithm is proposed to dynamically adjust the input of each neuron to alleviate the spike stall problem. The macro measures 0.084mm2 in a 40nm process with an energy efficiency of 68.51 TOPS/W and an area efficiency of 0.1956 TOPS/mm2 for 4b input and 8b weight.","PeriodicalId":48827,"journal":{"name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135845515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
For accelerating Binarized Neural Networks (BNNs), analog computing-based crossbar accelerators, utilizing XNOR gates and additional interface circuits, have been proposed. Such accelerators demand a large amount of analog-to-digital converters (ADCs) and registers, resulting in expensive designs. To increase the inference efficiency, the state of the art divides the interface circuit into an Analog Path (AP), utilizing (cheap) analog comparators, and a Digital Path (DP), utilizing (expensive) ADCs and registers. During BNN execution, a certain path is selectively triggered. Ideally, as inference via AP is more efficient, it should be triggered as often as possible. However, we reveal that, unless the number of weights is very small, the AP is rarely triggered. To overcome this, we propose a novel BNN inference scheme, called Local Thresholding Approximation (LTA). It approximates the global thresholdings in BNNs by local thresholdings. This enables the use of the AP through most of the execution, which significantly increases the interface circuit efficiency. In our evaluations with two BNN architectures, using LTA reduces the area by 42x and 54x, the energy by 2.7x and 4.2x, and the latency by 3.8x and 1.15x, compared to the state-of-the-art crossbar-based BNN accelerators.
{"title":"Unlocking Efficiency in BNNs: Global by Local Thresholding for Analog-Based HW Accelerators","authors":"Mikail Yayla;Fabio Frustaci;Fanny Spagnolo;Jian-Jia Chen;Hussam Amrouch","doi":"10.1109/JETCAS.2023.3315561","DOIUrl":"10.1109/JETCAS.2023.3315561","url":null,"abstract":"For accelerating Binarized Neural Networks (BNNs), analog computing-based crossbar accelerators, utilizing XNOR gates and additional interface circuits, have been proposed. Such accelerators demand a large amount of analog-to-digital converters (ADCs) and registers, resulting in expensive designs. To increase the inference efficiency, the state of the art divides the interface circuit into an Analog Path (AP), utilizing (cheap) analog comparators, and a Digital Path (DP), utilizing (expensive) ADCs and registers. During BNN execution, a certain path is selectively triggered. Ideally, as inference via AP is more efficient, it should be triggered as often as possible. However, we reveal that, unless the number of weights is very small, the AP is rarely triggered. To overcome this, we propose a novel BNN inference scheme, called Local Thresholding Approximation (LTA). It approximates the global thresholdings in BNNs by local thresholdings. This enables the use of the AP through most of the execution, which significantly increases the interface circuit efficiency. In our evaluations with two BNN architectures, using LTA reduces the area by 42x and 54x, the energy by 2.7x and 4.2x, and the latency by 3.8x and 1.15x, compared to the state-of-the-art crossbar-based BNN accelerators.","PeriodicalId":48827,"journal":{"name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10251514","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135443296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-13DOI: 10.1109/JETCAS.2023.3308285
{"title":"IEEE Circuits and Systems Society","authors":"","doi":"10.1109/JETCAS.2023.3308285","DOIUrl":"https://doi.org/10.1109/JETCAS.2023.3308285","url":null,"abstract":"","PeriodicalId":48827,"journal":{"name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/5503868/10251074/10251080.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50347857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the above article [1] , according to our institution, the text in project funding (bottom left on the first page) requires a minor modification. The following text:
在上述文章[1]中，根据我们的机构，项目资助中的文本（第一页左下角）需要稍作修改。以下文本：
{"title":"Corrections to “Digital implementation of Radial Basis Function Neural Networks Based on Stochastic Computing” [Mar 23 257-269]","authors":"Alejandro Morán Costoya;Luis Parrilla Roure;Miquel Roca;Joan Font-Rossello;Eugeni Isern;Vincent Canals","doi":"10.1109/JETCAS.2023.3287741","DOIUrl":"https://doi.org/10.1109/JETCAS.2023.3287741","url":null,"abstract":"In the above article \u0000<xref>[1]</xref>\u0000, according to our institution, the text in project funding (bottom left on the first page) requires a minor modification. The following text:","PeriodicalId":48827,"journal":{"name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/5503868/10251074/10251075.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50424424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-13DOI: 10.1109/JETCAS.2023.3308287
{"title":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems Publication Information","authors":"","doi":"10.1109/JETCAS.2023.3308287","DOIUrl":"https://doi.org/10.1109/JETCAS.2023.3308287","url":null,"abstract":"","PeriodicalId":48827,"journal":{"name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/5503868/10251074/10251078.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50347858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-13DOI: 10.1109/JETCAS.2023.3308283
{"title":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems Information for Authors","authors":"","doi":"10.1109/JETCAS.2023.3308283","DOIUrl":"https://doi.org/10.1109/JETCAS.2023.3308283","url":null,"abstract":"","PeriodicalId":48827,"journal":{"name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/5503868/10251074/10251077.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50347856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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