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IEEE Transactions on Very Large Scale Integration (VLSI) Systems Society Information 电气和电子工程师学会超大规模集成 (VLSI) 系统学会论文集信息
IF 2.8 2区 工程技术 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2024-10-25 DOI: 10.1109/TVLSI.2024.3474954
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引用次数: 0
IEEE Transactions on Very Large Scale Integration (VLSI) Systems Publication Information IEEE 超大规模集成 (VLSI) 系统论文集 出版信息
IF 2.8 2区 工程技术 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2024-10-25 DOI: 10.1109/TVLSI.2024.3474952
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引用次数: 0
IEEE Transactions on Very Large Scale Integration (VLSI) Systems Publication Information IEEE 超大规模集成 (VLSI) 系统论文集 出版信息
IF 2.8 2区 工程技术 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2024-09-26 DOI: 10.1109/TVLSI.2024.3457191
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引用次数: 0
IEEE Transactions on Very Large Scale Integration (VLSI) Systems Society Information 电气和电子工程师学会超大规模集成 (VLSI) 系统学会论文集信息
IF 2.8 2区 工程技术 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2024-09-26 DOI: 10.1109/TVLSI.2024.3457193
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引用次数: 0
The Error Analysis of Bit Weight Self-Calibration Methods for High-Resolution SAR ADCs 高分辨率 SAR ADC 比特权重自校准方法的误差分析
IF 2.8 2区 工程技术 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2024-09-19 DOI: 10.1109/TVLSI.2024.3458071
Yanhang Chen;Siji Huang;Qifeng Huang;Yifei Fan;Jie Yuan
High-resolution successive approximation register (SAR) analog-to-digital converters (ADCs) commonly need to calibrate their bit weights. Due to the nonidealities of the calibration circuits, the calibrated bit weights carry errors. This error could propagate during the calibration procedure. Due to the high precision requirement of these ADCs, such residue error commonly becomes the signal-to-noise-and-distortion ratio (SNDR) bottleneck of the overall ADC. This article presents an analysis of the residue error from bit weight self-calibration methods of high-resolution SAR ADCs. The major sources contributing to this error and the error reduction methods are quantitively analyzed. A statistical analysis of the noise-induced random error is developed. Our statistical model finds that the noise-induced random error follows the chi-square distribution. In practice, this random error is commonly reduced by repetitively measuring and averaging the calibrated bit weights. Our statistical model quantifies this bit weight error and leads to a clearer understanding of the error mechanism and design trade-offs. Following our chi-square model, the SNDR degradation due to the circuit noise during the calibration can be easily estimated without going through the time-consuming traditional transistor-level design and simulation process. The required repetition time can also be calculated. The bit-weight error models derived in this article are verified with measurement on a 16-bit SAR ADC design in a 180-nm CMOS process. Results from our model match both simulations and measurements well.
高分辨率逐次逼近寄存器(SAR)模数转换器(ADC)通常需要校准位权重。由于校准电路的非理想性,校准后的位权重会产生误差。这种误差可能在校准过程中传播。由于这些 ADC 的精度要求很高,这种残余误差通常会成为整个 ADC 的信噪比 (SNDR) 瓶颈。本文分析了高分辨率 SAR ADC 位权自校准方法产生的残差误差。文章定量分析了造成这一误差的主要来源和减少误差的方法。文章对噪声引起的随机误差进行了统计分析。我们的统计模型发现,噪声引起的随机误差遵循秩方分布。在实践中,这种随机误差通常通过重复测量和平均校准位权来减少。我们的统计模型量化了这种位权重误差,使人们对误差机制和设计权衡有了更清晰的认识。根据我们的秩方模型,校准过程中电路噪声导致的 SNDR 下降可以很容易地估算出来,而无需进行耗时的传统晶体管级设计和仿真过程。所需的重复时间也可以计算出来。本文推导出的位重误差模型在 180-nm CMOS 工艺的 16 位 SAR ADC 设计上进行了测量验证。我们的模型得出的结果与模拟和测量结果十分吻合。
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引用次数: 0
MCAIMem: A Mixed SRAM and eDRAM Cell for Area and Energy-Efficient On-Chip AI Memory MCAIMem:用于面积和能效高的片上人工智能存储器的混合 SRAM 和 eDRAM 单元
IF 2.8 2区 工程技术 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2024-09-18 DOI: 10.1109/TVLSI.2024.3439231
Duy-Thanh Nguyen;Abhiroop Bhattacharjee;Abhishek Moitra;Priyadarshini Panda
AI chips commonly employ SRAM memory as buffers for their reliability and speed, which contribute to high performance. However, SRAM is expensive and demands significant area and energy consumption. Previous studies have explored replacing SRAM with emerging technologies, such as nonvolatile memory, which offers fast read memory access and a small cell area. Despite these advantages, nonvolatile memory’s slow write memory access and high write energy consumption prevent it from surpassing SRAM performance in AI applications with extensive memory access requirements. Some research has also investigated embedded dynamic random access memory (eDRAM) as an area-efficient on-chip memory with similar access times as SRAM. Still, refresh power remains a concern, leaving the trade-off among performance, area, and power consumption unresolved. To address this issue, this article presents a novel mixed CMOS cell memory design that balances performance, area, and energy efficiency for AI memory by combining SRAM and eDRAM cells. We consider the proportion ratio of one SRAM and seven eDRAM cells in the memory to achieve area reduction using mixed CMOS cell memory. In addition, we capitalize on the characteristics of deep neural network (DNN) data representation and integrate asymmetric eDRAM cells to lower energy consumption. To validate our proposed MCAIMem solution, we conduct extensive simulations and benchmarking against traditional SRAM. Our results demonstrate that the MCAIMem significantly outperforms these alternatives in terms of area and energy efficiency. Specifically, our MCAIMem can reduce the area by 48% and energy consumption by $3.4times $ compared with SRAM designs, without incurring any accuracy loss.
人工智能芯片通常使用 SRAM 存储器作为缓冲器,其可靠性和速度有助于实现高性能。然而,SRAM 价格昂贵,需要大量的面积和能耗。以前的研究曾探讨过用非易失性存储器等新兴技术取代 SRAM,因为非易失性存储器读取内存速度快,单元面积小。尽管具有这些优势,但非易失性存储器的写入内存访问速度慢、写入能耗高,因此在具有大量内存访问要求的人工智能应用中,非易失性存储器的性能无法超越 SRAM。一些研究还将嵌入式动态随机访问存储器(eDRAM)作为一种面积效率高的片上存储器进行了研究,其访问时间与 SRAM 相似。然而,刷新功耗仍然是一个令人担忧的问题,性能、面积和功耗之间的权衡尚未解决。为解决这一问题,本文提出了一种新型混合 CMOS 单元存储器设计,通过结合 SRAM 和 eDRAM 单元,平衡了人工智能存储器的性能、面积和能效。我们考虑了存储器中一个 SRAM 和七个 eDRAM 单元的比例,以利用混合 CMOS 单元存储器实现面积缩减。此外,我们还利用深度神经网络(DNN)数据表示的特点,集成了非对称 eDRAM 单元,以降低能耗。为了验证我们提出的 MCAIMem 解决方案,我们进行了大量仿真,并以传统 SRAM 为基准进行了测试。结果表明,MCAIMem 在面积和能效方面明显优于这些替代方案。具体来说,与 SRAM 设计相比,我们的 MCAIMem 可以减少 48% 的面积和 3.4 美元/次的能耗,而且不会造成任何精度损失。
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引用次数: 0
Marmotini: A Weight Density Adaptation Architecture With Hybrid Compression Method for Spiking Neural Network Marmotini:采用混合压缩方法的尖峰神经网络权重密度自适应架构
IF 2.8 2区 工程技术 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2024-09-18 DOI: 10.1109/tvlsi.2024.3453897
Zilin Wang, Yi Zhong, Zehong Ou, Youming Yang, Shuo Feng, Guang Chen, Xiaoxin Cui, Song Jia, Yuan Wang
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引用次数: 0
A 22-nm 264-GOPS/mm$^{2}$ 6T SRAM and Proportional Current Compute Cell-Based Computing-in-Memory Macro for CNNs 用于 CNN 的 22 纳米 264-GOPS/mm$^{2}$ 6T SRAM 和基于比例电流计算单元的内存计算宏
IF 2.8 2区 工程技术 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2024-09-18 DOI: 10.1109/tvlsi.2024.3446045
Feiran Liu, Anran Yin, Chen Xue, Bo Wang, Zhongyuan Feng, Han Liu, Xiang Li, Hui Gao, Tianzhu Xiong, Xin Si
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引用次数: 0
An Interpolation-Free Fractional Motion Estimation Algorithm and Hardware Implementation for VVC 用于 VVC 的无插值分数运动估计算法和硬件实现
IF 2.8 2区 工程技术 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2024-09-17 DOI: 10.1109/tvlsi.2024.3455374
Shushi Chen, Leilei Huang, Zhao Zan, Xiaoyang Zeng, Yibo Fan
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引用次数: 0
55–100-GHz Enhanced Gilbert Cell Mixer Design in 22-nm FDSOI CMOS 采用 22 纳米 FDSOI CMOS 的 55-100-GHz 增强型吉尔伯特单元混频器设计
IF 2.8 2区 工程技术 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2024-09-17 DOI: 10.1109/tvlsi.2024.3454350
Kimi Jokiniemi, Kaisa Ryynänen, Joni Vähä, Elmo Kankkunen, Kari Stadius, Jussi Ryynänen
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引用次数: 0
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IEEE Transactions on Very Large Scale Integration (VLSI) Systems
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