|
[1] F. Akopyan, J. Sawada, A. Cassidy, R. Alvarez-Icaza, J. Arthur, P. Merolla, N. Imam, Y. Nakamura, P. Datta, G.-J. Nam, B. Taba, M. Beakes, B. Brezzo, J. B. Kuang, R. Manohar, W. P. Risk, B. Jackson, and D. S. Modha, “Truenorth: Design and tool flow of a 65 mw 1 million neuron programmable neurosynaptic chip,” IEEE Trans. on Computer-Aided Design of Integrated Circuits and Systems, vol. 34, no. 10, pp. 1537-1557, Aug. 2015. [2] M. Davies, N. Srinivasa, T.-H. Lin, G. Chinya, Y. Cao, Sri Harsha Choday, G. Dimou, P. Joshi, N. Imam, S. Jain, Y. Liao, C.-K. Lin, A. Lines, R. Liu, D. Mathaikutty, S. McCoy, A. Paul, J. Tse, G. Venkataramanan, Y.-H. Weng, A. Wild, Y. Yang, and H. Wang, “Loihi: A neuromorphic manycore processor with onchip learning,” IEEE Micro, vol. 38, no. 1, pp. 82-99, Jan.-Feb. 2018. [3] Cerebras Systems, “Wafer-Scale Deep Learning,” in Proc. IEEE Hot Chips 31 Symp. (HCS), Cupertino, pp. 1-31, Aug. 2019. [4] Cerebras Systems, “Cerebras Systems: Achieving Industry Best AI Performance Through A Systems Approach,” https://f.hubspotusercontent30.net/hubfs/896853 3/Cerebras-CS-2-Whitepaper.pdf, Apr. 2021. [5] N. P. Jouppi, C. Young, N. Patil, D. Patterson, G. Agrawal, R. Bajwa, S. Bates, S. Bhatia, N. Boden, A. Borchers, R. Boyle, P. Cantin, C. Chao, C. Clark, J. Coriell, M. Daley, M. Dau, J. Dean, B. Gelb, T. V. Ghaemmaghami, R. Gottipati, W. Gulland, R. Hagmann, R. C. Ho, D. Hogberg, J. Hu, R. Hundt, D. Hurt, J. Ibarz, A. Jaffey, A. Jaworski, A. Kaplan, H. Khaitan, A. Koch, N. Kumar, S. Lacy, J. Laudon, J. Law, D. Le, C. Leary, Z. Liu, K. Lucke, A. Lundin, G. MacKean, A. Maggiore, M. Mahony, K. Miller, R. Nagarajan, R. Narayanaswami, R. Ni, K. Nix, T. Norrie, M. Omernick, N. Penukonda, A. Phelps, J. Ross, A. Salek, E. Samadiani, C. Severn, G. Sizikov, M. Snelham, J. Souter, D. Steinberg, A. Swing, M. Tan, G. Thorson, B. Tian, H. Toma, E. Tuttle, V. Vasudevan, R. Walter, W. Wang, E. Wilcox, and H. Y. Doe, “In-datacenter performance analysis of a tensor processing unit,” in Proc. 44th Annu. Int. Symp. on computer architecture, Toronto, vol. 17, pp. 1-12, June 2017. [6] I. Takanami, and T. Horita, “A built-in circuit for self-repairing mesh-connected processor arrays by direct spare replacement,” in Proc. IEEE 18th Pacific Rim Int. Symp. on Dependable Computing, Niigata, pp. 96-104, Nov. 2012. [7] S.-Y. Kung, S.-N. Jean and C.-W. Chang, “Fault-tolerant array processors using single-track switches,” IEEE Trans. on Computers, vol. 38, no. 4, pp. 501-514, Jan. 1989. [8] Semiconductor Industry Association, “International Technology Roadmap for Semiconductors (ITRS), 2009 Edition,” Sematech, Hsinchu, Taiwan, Dec. 2009. [9] L.-T. Wang, C.-W. Wu, and X. Wen, Design for Testability: VLSI Test Principles and Architectures, Elsevier (Morgan Kaufmann), San Francisco, 2006. [10] M. Lee and C.-W. Wu, "Method for Repairing Memory and System Thereof", U.S. Patent No. 8095832B2, Jan. 2012. [11] M. Lee, L.-M. Denq, and C.-W. Wu, “BRAINS+: A memory built-in self-repair generator,” in Proc. 1st VLSI Test Technology Workshop (VTTW), Hsinchu, July 2007. [12] S.-K. Lu, C.-L. Yang, Y.-C. Hsiao, and C.-W. Wu, “Efficient BISR techniques for embedded memories considering cluster faults,” IEEE Trans. on VLSI Systems, vol. 18, no. 2, pp. 184-193, Feb. 2010. [13] P. Ohler, S. Hellebrand, and H. J. Wunderlich, “An integrated built-in test and repair approach for memories with 2-D redundancy,” in Proc. IEEE Eur. Test Symp. (ETS), Freiburg, pp. 91-96, May. 2007. [14] J. Lee, K. Park, and S. Kang, “An area-efficient built-in redundancy analysis for embedded memories with optimal repair rate using 2-D redundancy,” in Int. SoC Design Conf. (ISOCC), Busan, pp. 353-356, Nov. 2009. [15] S.-K. Lu, Y.-C. Tsai, C.-H. Hsu, K.-H. Wang, and C.-W. Wu, “Efficient built-in redundancy analysis for embedded memories with 2-D redundancy,” IEEE Trans. on VLSI Systems, vol. 14, no. 1, pp. 34-42, Jan. 2006. [16] Y.-J. Huang, D.-M. Chang, and J.-F. Li, “A built-in redundancy analysis scheme for self-repairable RAMs with two-level redundancy,” in Proc. IEEE Int. Symp. on Defect and Fault Tolerance in VLSI Systems (DFT), Arlington, pp. 362-370, Oct. 2006. [17] S. K. Thakur, R. A. Parekhji, and A. N. Chandorkar, “On-chip test and repair of memories for static and dynamic faults,” in Proc. IEEE Int. Test Conf. (ITC), Santa Clara, pp. 1-10, Oct. 2006. [18] C.-D. Huang, J.-F. Li, and T.-W. Tseng, “ProTaR: An infrastructure IP for repairing RAMs in system-on-chips,” IEEE Trans. On VLSI Systems, vol. 15, no. 10, pp. 1135-1143, Oct. 2007. [19] T.-W. Tseng and J.-F. Li, “A shared parallel built-in self-repair scheme for random access memories in SoCs,” in Proc. IEEE Int. Test Conf. (ITC), Santa Clara, pp. 1-9, Oct. 2008. [20] B. D. de Dinechin, “Kalray MPPA®: Massively parallel processor array: Revisiting DSP acceleration with the Kalray MPPA Manycore processor,” in Proc. IEEE Hot Chips 27 Symp. (HCS), Cupertino, pp. 1-27, Aug. 2015. [21] A. Lopich, and D. Piotr, “A SIMD cellular processor array vision chip with asynchronous processing capabilities,” IEEE Trans. on Circuits and Systems I: Regular Papers, vol. 58, no. 10, pp. 2420-2431. Oct. 2011. [22] C. Cheng, C.-T. Huang, J.-R. Huang, C.-W. Wu, C.-J. Wey, and M.-C. Tsai, “BRAINS: A BIST complier for embedded memories,” in Proc. IEEE Int. Symp. on Defect and Fault Tolerance in VLSI Systems (DFT), Yamanashi, pp. 299-307, Oct. 2000. [23] K.-L. Cheng, C.-M. Hsueh, J.-R. Huang, J.-C. Yeh, C.-T. Huang, and C.-W. Wu, “Automatic generation of memory built-in self-test cores for system-on-chip,” in Proc. 10th IEEE Asian Test Symp. (ATS), Kyoto, pp. 91-96, Nov. 2001. [24] C.-T. Huang, The user guide of programmable memory BIST compiler, version 1.0, Oct. 2001. [25] C.-T. Huang, C.-F. Wu, J.-F. Li, and C.-W. Wu, “Built-in redundancy analysis for memory yield improvement,” IEEE Trans. on Reliability, vol. 52, no. 4, pp. 386-399, Dec. 2003. [26] M. Lee, L.-M. Denq, and C.-W. Wu, “A memory built-in self-repair scheme based on configurable spares.” IEEE Trans. on Computer-Aided Design of Integrated Circuits and Systems, vol. 30, no. 6, pp. 919-929, June 2011. [27] J. P. Bickford, R. Rosner, E. Hedberg, J. W. Yoder, and T. S. Barnett, “SRAM Redundancy - Silicon Area versus Number of Repairs Trade-off,” in Proc. IEEE/SEMI ASMC, Cambridge, pp. 387-392, May 2008. [28] C.-F. Wu, C.-T. Huang, and C.-W. Wu, “RAMSES: A fast memory fault simulator,” in Proc. IEEE Int. Symp. on Defect and Fault Tolerance in VLSI Systems (DFT), Albuquerque, pp. 165-173, Nov. 1999.
|