Mobile QR Code QR CODE

REFERENCES

1 
J. Xiao, “A high-accuracy and ultra-energy-efficient keyword spotting processor with adaptive noise suppression and multi-user speaker verification”, IEEE Journal of Solid-State Circuits, 2025.DOI
2 
I. McGraw, R. Prabhavalkar, R. Alvarez, M. Gonzalez Arenas, K. Rao, D. Rybach, O. Alsharif, H. Sak, A. Gruenstein, F. Beaufays, and C. Parada, “Personalized speech recognition on mobile devices”, Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5955-5959, 2016.DOI
3 
F. Tan, W.-H. Yu, J. Lin, K.-F. Un, R. P. Martins, and P.-I. Mak, “A 1.8% FAR, 2 ms decision latency, 1.73 nJ/decision keyword-spotting (KWS) chip incorporating transfer-computing speaker verification, hybrid-IF-domain computing and scalable 5T-SRAM”, IEEE International Solid-State Circuits Conference (ISSCC), 2024.DOI
4 
H. Yang, J.-H. Seol, R. Rothe, Z. Fan, Q. Zhang, H.-S. Kim, D. Blaauw, and D. Sylvester, “A 1.5-$\mu$W fully-integrated keyword spotting SoC in 28-nm CMOS with skip-RNN and fast-settling analog frontend for adaptive frame skipping”, IEEE Journal of Solid-State Circuits, vol. 59, no. 1, pp. 29-39, 2024.DOI
5 
K. Kim, C. Gao, R. Graa, I. Kiselev, H.-J. Yoo, T. Delbruck, and S.-C. Liu, “A 23-$\mu$W keyword spotting IC with ring-oscillator-based time-domain feature extraction”, IEEE Journal of Solid-State Circuits, vol. 57, no. 11, pp. 3298-3311, 2022.DOI
6 
Y. Luo, J. Vanderhaegen, O. Rybakov, M. Kraemer, N. Warren, and S. Yu, “A FeFET-based ADC offset robust compute-in-memory architecture for streaming keyword spotting (KWS)”, IEEE Transactions on Emerging Topics in Computing, vol. 12, no. 1, pp. 23-34, 2024.DOI
7 
R. Guo, Y. Liu, S. Zheng, S.-Y. Wu, P. Ouyang, W.-S. Khwa, X. Chen, J.-J. Chen, X. Li, L. Liu, and M.-F. Chang, “A 5.1 pJ/neuron 127.3 $\mu$s/inference RNN-based speech recognition processor using 16 computing-in-memory SRAM macros in 65-nm CMOS”, Symposium on VLSI Circuits, pp. C120-C121, 2019.DOI
8 
S. Kim, S. Um, W. Jo, J. Lee, S. Ha, Z. Li, and H.-J. Yoo, “Scaling-CIM: eDRAM in-memory-computing accelerator with dynamic-scaling ADC and adaptive analog operation”, IEEE Journal of Solid-State Circuits, vol. 59, no. 8, pp. 2694-2705, 2024.DOI
9 
S. Xie, C. Ni, A. Sayal, P. Jain, F. Hamzaoglu, and J. P. Kulkarni, “eDRAM-CIM: Reconfigurable charge-domain compute-in-memory design with embedded dynamic random access memory array realizing adaptive data converters”, IEEE Journal of Solid-State Circuits, vol. 59, no. 6, pp. 1950-1961, 2024.DOI
10 
S. Kim, Z. Li, S. Um, W. Jo, S. Ha, J. Lee, S. Kim, D. Han, and H.-J. Yoo, “DynaPlasia: An eDRAM in-memory computing-based reconfigurable spatial accelerator with triple-mode cell”, IEEE Journal of Solid-State Circuits, vol. 59, no. 1, pp. 102-115, 2024.DOI
11 
H. Jeong, S. Kim, K. Park, J. Jung, and K. J. Lee, “A ternary neural network computing-in-memory processor with 16T1C bitcell architecture”, IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 70, no. 5, pp. 1739-1743, 2023.DOI
12 
S. Mittermaier, L. Krzinger, B. Waschneck, and G. Rigoll, “Small-footprint keyword spotting on raw audio data with sinc-convolutions”, Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). Barcelona, Spain, pp. 7454-7458, 2020.DOI
13 
J. M. Mayor-Torres, M. Ravanelli, S. E. Medina-DeVilliers, M. D. Lerner, and G. Riccardi, “Interpretable SincNet-based deep learning for emotion recognition from EEG brain activity”, arXiv:2107.10790, 2021.DOI
14 
J. Abel, and T. Fingscheidt, “Artificial speech bandwidth extension using deep neural networks for wideband spectral envelope estimation”, IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 26, no. 1, pp. 71-83, 2018.DOI
15 
J.-H. Kim, J. Lee, J. Lee, J. Heo, and J.-Y. Kim, “Z-PIM: A sparsity aware processing-in-memory architecture with fully variable weight bit precision for energy-efficient deep neural networks”, IEEE Journal of Solid-State Circuits, vol. 56, no. 4, pp. 1093-1104, 2021.DOI
16 
S. Angizi, Z. He, and D. Fan, “DIMA: A depthwise CNN in-memory accelerator”, Proceedings of the IEEE/ACM International Conference on Computer-Aided Design (ICCAD), pp. 1-8, 2018.DOI
17 
S. Negi, “Algorithm hardware co-design for ADC-less compute in-memory accelerator”, IEEE Transactions on Circuits and Systems for Artificial Intelligence, vol. 1, no. 2, pp. 191-203, 2024.DOI
18 
I. Yeo, W. He, Y.-C. Luo, S. Yu, and J.-S. Seo, “A dynamic power only compute-in-memory macro with power-of-two nonlinear SAR ADC for nonvolatile ferroelectric capacitive crossbar array”, IEEE Solid-State Circuits Letters, vol. 7, pp. 70-73, 2024.DOI
19 
J. Heo, J. Kim, S. Lim, W. Han, and J.-Y. Kim, “T-PIM: An energy efficient processing-in-memory accelerator for end-to-end on-device training”, IEEE Journal of Solid-State Circuits, vol. 58, no. 3, pp. 600-613, 2023.DOI
20 
X. Sun, S. Yin, X. Peng, R. Liu, J.-S. Seo, and S. Yu, “XNOR-RRAM: A scalable and parallel resistive synaptic architecture for binary neural networks”, Design, Automation and Test in Europe Conference and Exhibition (DATE). Dresden, Germany, pp. 1423-1428, 2018.DOI
21 
S. Ha, S. Kim, D. Han, S. Um, and H.-J. Yoo, “A 36.2 dB high SNR and PVT/leakage-robust eDRAM computing-in-memory macro with segmented BL and reference cell array”, IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 69, no. 5, pp. 2433-2437, 2022.DOI
22 
D. Kim, H. Jeong, S. Kim, H. Yoon, and K. J. Lee, “A multibit ReRAM computing-in-memory processor with adaptive decision level nonlinear ADC for ultra-low-energy keyword spotting in mobile devices”, IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 73, no. 2, pp. 862-874, 2026.DOI
23 
P. Warden, “Speech commands: A dataset for limited-vocabulary speech recognition”, arXiv:1804.03209, 2018.DOI
24 
A. Coucke, M. Chlieh, T. Gisselbrecht, D. Leroy, M. Poumeyrol, and T. Lavril, “Efficient keyword spotting using dilated convolutions and gating”, arXiv:1811.07684, 2018.DOI
25 
J. Salamon, C. Jacoby, and J. P. Bello, “A dataset and taxonomy for urban sound research”, Proceedings of the 22nd ACM International Conference on Multimedia. Orlando, FL, USA, pp. 1041-1044, 2014.DOI