Publications

Journal Articles

Recent Articles in Conference Proceedings

Book Chapters and Technical Reports

Journals

• [J41] J.-F. Zhang, C.-H. Lu, and Z. Zhang, “TetriX: Flexible architecture and optimal mapping for tensorized neural network processing,” to appear in IEEE Trans. Computers, 2024.
• [J40] W. Tang*, S.-G. Cho*, T. T. Hoang*, J. Botimer, W. Q. Zhu, C.-C. Chang, C.-H. Lu, J. Zhu, Y. Tao, T. Wei, N. K. Motwani, M. Yalamanchi, R. Yarlagadda, S. Kale, M. Flanigan, A. Chan, T. Tran, S. Shumarayev, and Z. Zhang, “Arvon: A heterogeneous system-in-package integrating FPGA and DSP chiplets for versatile workload acceleration,” IEEE J. Solid-State Circuits, vol. 59, no. 4, pp. 1235-1245, Apr. 2024. (* Equal contribution)
• [J39] F.-H. Meng, Y. Wu, Z. Zhang, and W. D. Lu, “TT-CIM: Tensor train decomposition for neural network in RRAM-based compute-in-memory systems,” IEEE Trans. Circuits Syst. I, Reg. Papers, vol. 71, no. 3, pp. 1172-1183, Mar. 2024.
• [J38] J.-F. Zhang and Z. Zhang, “Machine learning hardware design for efficiency, flexibility, and scalability,” IEEE Circuits and Systems Mag., vol. 23, no. 3, pp. 35-53, Oct. 2023 (Invited).
• [J37] R. Pinkham*, J. Erhardt*, B. De Salvo, A. Berkovich, and Z. Zhang, “ANSA: Adaptive near-sensor architecture for dynamic DNN processing in compact form factors,” IEEE Trans. Circuits Syst. I, Reg. Papers, vol. 70, no. 3, pp. 1256-1269, Mar. 2023. (* Equal contribution)
• [J36] J. Zhu, W. Tang, C.-E. Lee, H. Ye, E. McCreath, and Z. Zhang, “VOTA: A heterogeneous multicore visual object tracking accelerator using correlation filters,” IEEE J. Solid-State Circuits, vol. 57, no. 11, pp. 3490-3502, Nov. 2022.
• [J35] X. Wang, R. Pinkham, M. A. Zidan, F.-H. Meng, M. P. Flynn, Z. Zhang, and W. D. Lu, “TAICHI: A tiled architecture for in-memory computing and heterogeneous integration,” IEEE Trans. Circuits Syst. II, Exp. Briefs, vol. 69, no. 2, pp. 559-563, Feb. 2022.
• [J34] R. Pinkham, A. Berkovich, and Z. Zhang, “Near-sensor distributed DNN processing for augmented and virtual reality,” IEEE J. Emerging Sel. Topics in Circuits Syst., vol. 11, no. 4, pp. 663-676, Dec. 2021.
• [J33] W. Tang, C.-H. Chen, and Z. Zhang, “A 0.58mm2 2.76Gb/s 79.8pJ/b 256-QAM message-passing detector for a 128×32 massive MIMO uplink system,” IEEE J. Solid-State Circuits, vol. 56, no. 6, pp. 1722-1731, Jun. 2021.
• [J32] J.-F. Zhang, C.-E. Lee, C. Liu, Y. S. Shao, S. W. Keckler, and Z. Zhang, “SNAP: An efficient sparse neural acceleration processor for unstructured sparse deep neural network inference,” IEEE J. Solid-State Circuits, vol. 56, no. 2, pp. 636-647, Feb. 2021.
• [J31] Y. Tao, S.-G. Cho, and Z. Zhang, “A configurable successive-cancellation list polar decoder using split-tree architecture,” IEEE J. Solid-State Circuits, vol. 56, no. 2, pp. 612-623, Feb. 2021.
• [J30] J. M. Correll, V. Bothra, F. Cai, Y. Lim, S. H. Lee, S. Lee, W. D. Lu. Z. Zhang, and M. P. Flynn, “A fully-integrated reprogrammable CMOS-RRAM compute-in-memory coprocessor for neuromorphic applications,” IEEE J. Exploratory Solid-State Computational Devices and Circuits, vol. 6, no. 1, pp. 36-44, Jun. 2020.
• [J29] T. Chen, J. Botimer, T. Chou, and Z. Zhang, “A 1.87mm2 56.9GOPS accelerator for solving partial differential equations,” IEEE J. Solid-State Circuits, vol. 55, no. 6, pp. 1709-1718, Jun. 2020.
• [J28] Y. Tao, S. Sun, and Z. Zhang, “Efficient post-processors for improving error-correcting performance of LDPC codes,” IEEE Trans. Circuits Syst. I, Reg. Papers, vol. 66, no. 10, pp. 4032-4043, Oct. 2019.
• [J27] F. Cai, J. M. Correll, S. H. Lee, Y. Lim, V. Bothra, Z. Zhang, M. P. Flynn, W. D. Lu, “A fully integrated reprogrammable memristor-CMOS system for efficient multiply-accumulate operations,” Nature Electronics, vol. 2, no. 7, pp. 290-299, Jul. 2019.
• [J26] T. Chen, C.-E. Lee, C. Liu, and Z. Zhang, “A 135mW 1.70TOPS sparse video sequence inference SoC for action classification,” IEEE J. Solid-State Circuits, vol. 54, no. 7, pp. 2081-2090, Jul. 2019.
• [J25] W. Tang, C.-H. Chen, and Z. Zhang, “A 2.4mm2 130mW MMSE-nonbinary LDPC iterative detector-decoder for 4×4 256-QAM MIMO in 65nm CMOS,” IEEE J. Solid-State Circuits, vol. 54, no. 7, pp. 2070-2080, Jul. 2019.
• [J24] C. Liu, S.-G. Cho, and Z. Zhang, “A 2.56-mm2 718GOPS configurable spiking convolutional sparse coding accelerator in 40-nm CMOS,” IEEE J. Solid-State Circuits, vol. 53, no. 10, pp. 2818-2827, Oct. 2018.
• [J23] M. Zidan, Y. Jeong, J. H. Shin, C. Du, Z. Zhang, and W. Lu, “Field-programmable crossbar array (FPCA) for reconfigurable computing,” IEEE Trans. Multi-Scale Computing Syst., vol. 4, no. 4, pp. 698-710, Oct.-Dec. 2018.
• [J22] S. Song, K. D. Choo, T. Chen, S. Jang, M. P. Flynn, and Z. Zhang, “A maximum-likelihood sequence detection powered ADC-based serial link,” IEEE Trans. Circuits Syst. I, Reg. Papers, vol. 65, no. 7, pp. 2269-2278, Jul. 2018.
• [J21] Z. Li, Q. Dong, M. Saligane, B. Kempke, L. Gong, Z. Zhang, R. Dreslinski, D. Sylvester, D. Blaauw, and H.-S. Kim, “A 1920 x 1080 30-frames/s 2.3 TOPS/W stereo-depth processor for energy-efficient autonomous navigation of micro aerial vehicles,” IEEE J. Solid-State Circuits., vol. 53, no. 1, pp. 76-90, Jan. 2018.
• [J20] S. Sun and Z. Zhang, “Designing practical polar codes using simulation-based bit selection,” IEEE J. Emerging Sel. Topic Circuits Syst., vol. 7, no. 4, pp. 594-603, Dec. 2017.
• [J19] P. M. Sheridan, F. Cai, C. Du, W. Ma, Z. Zhang, and W. D. Lu, “Sparse coding with memristor networks,” Nature Nanotechnology, vol. 12, no. 8, pp. 784-789, May 2017.
• [J18] J. Bell, P. Knag, S. Sun, Y. Lim, T. Chen, J. Fredenburg, C.-H. Chen, C. Zhai, A. Rocca, N. Collins, A. Tamez, J. Pernillo, J. Correll, Z. Zhang, and M. P. Flynn, “A 1.5GHz 6.144Tcorrelations/s 64×64 cross-correlator with 128 integrated ADCs for real-time synthetic aperture imaging,” IEEE J. Solid-State Circuits, vol. 52, no. 5, pp. 1450-1457, May 2017.
• [J17] F. Sheikh, C.-H. Chen, D. Yoon, B. Alexandrov, K. Bowman, A. Chun, H. Alavi, and Z. Zhang, “3.2 Gbps channel-adaptive configurable MIMO detector for multi-mode wireless communication,” J. Signal Process. Syst., vol. 84, no. 3, pp. 295-307, Jan. 2016.
• [J16] C.-H. Chen, S. Song, and Z. Zhang, “An FPGA-based transient error simulator for resilient circuit and system design and evaluation,” IEEE Trans. Circuits Syst. II, Exp. Briefs, vol. 62, no. 5, pp. 471-475, May 2015.
• [J15] P. Knag, J. K. Kim, T. Chen, and Z. Zhang, “A sparse coding neural network ASIC with on-chip learning for feature extraction and encoding,” IEEE J. Solid-State Circuits, vol. 50, no. 4, pp. 1070-1079, Apr. 2015.
• [J14] Y. S. Park, Y. Tao, and Z. Zhang, “A fully parallel nonbinary LDPC decoder with fine-grained dynamic clock gating,” IEEE J. Solid-State Circuits, vol. 50, no. 2, pp. 464-475, Feb. 2015.
• [J13] Y.-P. Chen, D. Jeon, Y. Lee, Y. Kim, Z. Foo, I. Lee, N. Langhals, G. Kruger, H. Oral, O. Berenfeld, Z. Zhang, D. Blaauw, and D. Sylvester, “An injectable 64nW ECG mixed-signal SoC in 65nm for arrhythmia monitoring,” IEEE J. Solid-State Circuits, vol. 50, no. 1, pp. 375-390, Jan. 2015.
• [J12] C.-H. Chen, P. Knag, and Z. Zhang, “Characterization of heavy-ion-induced single-event effects in 65 nm bulk CMOS ASIC test chips,” IEEE Trans. Nucl. Sci., vol. 61, no. 5, pp. 2694-2701, Oct. 2014.
• [J11] J. K. Kim, P. Knag, T. Chen, and Z. Zhang, “Efficient hardware architecture for sparse coding,” IEEE Trans. Signal Process., vol. 62, no. 16, pp. 4173-4186, Aug. 2014.
• [J10] C.-H. Chen, D. Blaauw, D. Sylvester, and Z. Zhang, “Design and evaluation of confidence-driven error-resilient systems,” IEEE Trans. Very Large Scale Integr. (VLSI) Syst., vol. 22, no. 8, pp. 1727-1737, Aug. 2014.
• [J9] D. Jeon, M. Henry, Y. Kim, I. Lee, Z. Zhang, D. Blaauw, and D. Sylvester, “An energy efficient full-frame feature extraction accelerator with shift-latch FIFO in 28nm CMOS,” IEEE J. Solid-State Circuits, vol. 49, no. 5, pp. 1247-1284, May 2014.
• [J8] Y. S. Park, D. Blaauw, D. Sylvester, and Z. Zhang, “Low-power high-throughput LDPC decoder using non-refresh embedded DRAM,” IEEE J. Solid-State Circuits, vol. 49, no. 3, pp. 783-794, Mar. 2014.
• [J7] P. Knag, W. Lu, and Z. Zhang, “A native stochastic computing architecture enabled by memristors,” IEEE Trans. Nanotechnol., vol. 13, no. 2, pp. 283-293, Mar. 2014.
• [J6] D. Jeon, M. Seok, Z. Zhang, D. Blaauw, and D. Sylvester, “Design methodology for voltage-overscaled ultra-low-power systems,” IEEE Trans. Circuits Syst. II, Exp. Briefs, vol. 59, no. 12, pp. 952-956, Dec. 2012.
• [J5] J. K. Kim, J. A. Fessler, and Z. Zhang, “Forward-projection architecture for fast iterative image reconstruction in X-ray CT,” IEEE Trans. Signal Process., vol. 60, no. 10, pp. 5508-5518, Oct. 2012.
• [J4] Z. Zhang, V. Anantharam, M. J. Wainwright, and B. Nikolic, “An efficient 10GBASE-T Ethernet LDPC decoder design with low error floors,” IEEE J. Solid-State Circuits, vol. 45, no. 4, pp. 843-855, Apr. 2010.
• [J3] L. Dolecek, Z. Zhang, V. Anantharam, M. J. Wainwright, and B. Nikolic, “Analysis of absorbing sets and fully absorbing sets of array-based LDPC codes,” IEEE Trans. Inf. Theory, vol. 56, no. 1, pp. 181-201, Jan. 2010.
• [J2] Z. Zhang, L. Dolecek, B. Nikolic, V. Anantharam, and M. J. Wainwright, “Design of LDPC decoders for improved low error rate performance: quantization and algorithm choices,” IEEE Trans. Commun., vol. 57, no. 11, pp. 3258-3268, Nov. 2009.
• [J1] L. Dolecek, P. Lee, Z. Zhang, V. Anantharam, B. Nikolic, and M. Wainwright, “Predicting error floors of LDPC codes: deterministic bounds and estimates,” IEEE J. Sel. Areas Commun., vol. 27, no. 6, pp. 908-917, Aug. 2009.

Recent Conference Proceedings (2020- )

• [C72] D. Kam, S. Yun, J. Choe, Z. Zhang, N. Lee, and Y. Lee, “A 21.9ns 15.7Gb/s/mm2 (128, 15) BOSS FEC decoder for 5G/6G URLLC applications,” in IEEE Int. Solid-State Circuits Conf. (ISSCC), San Francisco, CA, Feb. 2024.
• [C71] T. Chou, F. Garcia-Redondo, P. Whatmough, and Z. Zhang, “AR-PIM: An adaptive-range processing-in-memory architecture,” in IEEE Int. Symp. Low Power Electron. Des. (ISLPED), Vienna, Austria, Aug. 2023.
• [C70] W. Tang*, S.-G. Cho*, T. T. Hoang*, J. Botimer, W. Q. Zhu, C.-C. Chang, C.-H. Lu, J. Zhu, Y. Tao, T. Wei, N. K. Motwani, M. Yalamanchi, R. Yarlagadda, S. Kale, M. Flanigan, A. Chan, T. Tran, S. Shumarayev, and Z. Zhang, “Arvon: A heterogeneous SiP integrating a 14nm FPGA and two 22nm 1.8TFLOPS/W DSPs with 1.7Tbps/mm2 AIB 2.0 interface to provide versatile workload acceleration,” in Symp. VLSI Circuits, Kyoto, Japan, Jun. 2023. (* Equal contribution)
• [C69] J. Zhu*, Y. Tao*, and Z. Zhang, “eNODE: Energy-efficient and low-latency edge inference and training of neural ODEs,” in IEEE Int. Symp. High-Performance Computer Architecture (HPCA), Montreal, Canada, Feb. 2023. (* Equal contribution)
• [C68] J. Correll, L. Jie, S. Song, S. Lee, J. Zhu, W. Tang, L. Wormald, J. Erhardt, N. Breil, R. Quon, D. Kamalanathan, S. Krishan, M. Chudzik, Z. Zhang, W. Lu, M. Flynn, “An 8-bit 20.7 TOPS/W multi-level cell ReRAM-based compute engine,” in Symp. VLSI Circuits, Honolulu, HI, Jun. 2022.
• [C67] T. Chou, W. Tang, M. Rotaru, C. Liu, R. Dutta, S. Lim, D. Ho, S. Bhattacharya, and Z. Zhang, “NetFlex: A 22nm multi-chiplet perception accelerator in high-density fan-out wafer-level packaging,” in Symp. VLSI Circuits, Honolulu, HI, Jun. 2022.
• [C66] Y. Tao and Z. Zhang, “DNC-aided SCL-flip decoding of polar codes,” in IEEE Global Commun. Conf. (GLOBECOM), Dec. 2021. (Best Paper Award)
• [C65] J.-F. Zhang and Z. Zhang, “Point-X: A spatial-locality-aware architecture for energy-efficient graph-based point-cloud deep learning,” in IEEE/ACM Int. Symp. Microarchitecture (MICRO), Oct. 2021.
• [C64] Y. Tao and Z. Zhang, “HiMA: A fast and scalable history-based memory access engine for differentiable neural computer,” in IEEE/ACM Int. Symp. Microarchitecture (MICRO), Oct. 2021.
• [C63] J.-F. Zhang and Z. Zhang, “Exploration of energy-efficient architecture for graph-based point-cloud deep learning,” in IEEE Workshop Signal Process. Syst. (SIPS), Oct. 2021 (Invited).
• [C62] S.-G. Cho, W. Tang, C. Liu, and Z. Zhang, “PETRA: A 22nm 6.97TFLOPS/W AIB-enabled configurable matrix and convolution accelerator integrated with an Intel Stratix 10 FPGA,” in Symp. VLSI Circuits, Jun. 2021.
• [C61] J. Zhu, W. Tang, C.-E. Lee, H. Ye, E. McCreath, and Z. Zhang, “VOTA: A 2.45TFLOPS/W heterogeneous multi-core visual object tracking accelerator based on correlation filters,” in Symp. VLSI Circuits, Jun. 2021.
• [C60] M. Rotaru, W. Tang, R. Dutta, and Z. Zhang, “Design and development of high density fan-out wafer level package (HD-FOWLP) for deep neural network (DNN) chiplet accelerators using advanced interface bus (AIB),” in IEEE Electronic Components and Technology Conf. (ECTC), Jun. 2021.
• [C59] C. Liu, J. Botimer, and Z. Zhang, “A 256Gb/s/mm-shoreline AIB-compatible 16nm FinFET CMOS chiplet for 2.5D integration with Stratix 10 FPGA on EMIB and tiling on silicon interposer,” in IEEE Custom Integrated Circuits Conf. (CICC), Apr. 2021. (Best Student Paper Candidate)
• [C58] R. Pinkham, S. Zeng, and Z. Zhang, “QuickNN: Memory and performance optimization of k-d tree based nearest neighbor search for 3D point clouds,” in IEEE Int. Symp. High-Performance Computer Architecture (HPCA), San Diego, CA, Feb. 2020.

Conference Proceedings (2010-2019)

• [C57] T. Chou, W. Tang, J. Botimer, and Z. Zhang, “CASCADE: Connecting RRAMs to extend analog dataflow in an end-to-end in-memory processing paradigm,” in IEEE/ACM Int. Symp. Microarchitecture (MICRO), Columbus, OH, Oct. 2019, pp. 114-125.
• [C56] J.-F. Zhang, C.-E. Lee, C. Liu, Y. S. Shao, S. W. Keckler, and Z. Zhang, “SNAP: A 1.67 – 21.55TOPS/W sparse neural acceleration processor for unstructured sparse deep neural network inference in 16nm CMOS,” in Symp. VLSI Circuits, Kyoto, Japan, Jun. 2019, pp. 306-307.
• [C55] Y. Tao, S.-G. Cho, and Z. Zhang, “A 3.25Gb/s, 13.2pJ/b, 0.64mm2 configurable successive-cancellation list polar decoder using split-tree architecture in 40nm CMOS,” in Symp. VLSI Circuits, Kyoto, Japan, Jun. 2019, pp. 240-241.
• [C54] T. Chen, J. Botimer, T. Chou, and Z. Zhang, “An SRAM-based accelerator for solving partial differential equations,” in IEEE Custom Integrated Circuits Conf. (CICC), Austin, TX, Apr. 2019.
• [C53] S.-G. Cho, E. Beigné, and Z. Zhang, “A 2048-neuron spiking neural network accelerator with neuro-inspired pruning and asynchronous network on chip in 40nm CMOS,” in IEEE Custom Integrated Circuits Conf. (CICC), Austin, TX, Apr. 2019.
• [C52] C. Liu and Z. Zhang, “Inference and learning hardware architecture for neuro-inspired sparse coding Algorithm,” in IEEE Biomedical Circuits Syst. Conf. (BioCAS), Cleveland, OH, Oct. 2018. (Invited)
• [C51] H.-S. Wu, Z. Zhang, and M. Papaefthymiou, “A 0.23mW heterogeneous deep-learning processor supporting dynamic execution of conditional neural networks,” in European Solid-State Circuits Conf. (ESSCIRC), Dresden, Germany, Sep. 2018.
• [C50] S. Song, W. Tang, T. Chen, and Z. Zhang, “LEIA: A 2.05mm2 140mW lattice encryption instruction accelerator in 40nm CMOS,” in IEEE Custom Integrated Circuits Conf. (CICC), San Diego, CA, Apr. 2018.
• [C49] C.-E. Lee, Y. S. Shao, J.-F. Zhang, A. Parashar, J. Emer, S. W. Keckler, and Z. Zhang, “Stitch-X: An accelerator architecture for exploiting unstructured sparsity in deep neural networks,” in SysML Conf., Stanford, CA, Feb. 2018.
• [C48] W. Tang, H. Prabhu, L. Liu, V. Öwall, and Z. Zhang, “A 1.8Gb/s 70.6pJ/b 128×16 link-adaptive near-optimal massive MIMO detector in 28nm UTBB-FDSOI,” in IEEE Int. Solid-State Circuits Conf. (ISSCC), San Francisco, CA, Feb. 2018, pp. 224-225.
• [C47] S. Sun, S.-G. Cho, and Z. Zhang, “Post-processing methods for improving coding gain in belief propagation decoding of polar codes,” in IEEE Global Commun. Conf. (GLOBECOM), Singapore, Dec. 2017.
• [C46] C. Liu, S.-G. Cho, and Z. Zhang, “A 2.56mm2 718GOPS configurable spiking convolutional sparse coding processor in 40nm CMOS,” in IEEE Asian Solid-State Circuits Conf. (A-SSCC), Seoul, Korea, Nov. 2017, pp. 233-236.
• [C45] S. Lu, Z. Zhang, and M. Papaefthymiou, “A 1.25pJ/bit 0.048mm2 AES core with DPA resistance for IoT devices,” in IEEE Asian Solid-State Circuits Conf. (A-SSCC), Seoul, Korea, Nov. 2017, pp. 65-68.
• [C44] J. K. Kim, P. Knag, T. Chen, C. Liu, C.-E. Lee, and Z. Zhang, “High-performance spiking neural net accelerators for embedded computer vision applications,” in IEEE SOI-3D-Subthreshold Microelectronics Technology Unified Conf. (S3S), San Francisco, CA, Oct. 2017. (Invited)
• [C43] C.-E. Lee, T. Chen, and Z. Zhang, “A 127mW 1.63TOPS sparse spatio-temporal cognitive SoC for action classification and motion tracking in videos,” in Symp. VLSI Circuits, Kyoto, Japan, Jun. 2017, pp. 226-227.
• [C42] F. N. Buhler, P. Brown, J. Li, T. Chen, Z. Zhang, and M. P. Flynn, “A 3.43TOPS/W 48.9pJ/pixel 50.1nJ/classification 512 analog neuron sparse coding neural network with on-chip learning and classification in 40nm CMOS,” in Symp. VLSI Circuits, Kyoto, Japan, Jun. 2017, pp. 30-31.
• [C41] H.-S. Wu, Z. Zhang, and M. C. Papaefthymiou, “A 13.8µW binaural dual-microphone digital ANSI S1.11 filter bank for hearing aids with zero-short-circuit-current logic in 65nm CMOS,” in IEEE Int. Solid-State Circuits Conf. (ISSCC), San Francisco, CA, Feb. 2017, pp. 348-349.
• [C40] Z. Li, Q. Dong, M. Saligane, B. Kempke, S. Yang, Z. Zhang, R. Dreslinski, D. Sylvester, D. Blaauw, and H. S. Kim, “A 1920×1080 30fps 2.3TOPS/W stereo-depth processor for robust autonomous navigation,” in IEEE Int. Solid-State Circuits Conf. (ISSCC), San Francisco, CA, Feb. 2017, pp. 62-63.
• [C39] S. Lu, Z. Zhang, and M. Papaefthymiou, “A 5.5GHz 0.84TOPS/mm2 neural network engine with stream architecture and resonant clock mesh,” in IEEE Asian Solid-State Circuits Conf. (A-SSCC), Toyama, Japan, Nov. 2016, pp. 133-136.
• [C38] S. Sun, S.-G. Cho, and Z. Zhang, “Error patterns in belief propagation decoding of polar codes and their mitigation methods,” in Asilomar Conf. Signals, Syst., Comput., Pacific Grove, CA, Nov. 2016. (Invited)
• [C37] T. Gaier, P. Kangaslahti, B. Lambrigtsen, I. Ramos-Perez, A. Tanner, D. McKague, C. Ruf, M. Flynn, Z. Zhang, R. Backhus, and D. Austerberry, “A 180 GHz prototype for a geostationary microwave imager/sounder-GEOSTAR-III,” in IEEE Int. Geosci. Remote Sens. Symp. (IGARSS), Beijing, China, Jul. 2016, pp. 2021-2023.
• [C36] W. Tang, C.-H. Chen, and Z. Zhang, “A 0.58mm2 2.76Gb/s 79.8pJ/b 256-QAM massive MIMO message-passing detector,” in Symp. VLSI Circuits, Honolulu, HI, Jun. 2016.
• [C35] P. Knag, C. Liu, and Z. Zhang, “A 1.40mm2 141mW 898GOPS sparse neuromorphic processor in 40nm CMOS,” in Symp. VLSI Circuits, Honolulu, HI, Jun. 2016.
• [C34] S. Sun and Z. Zhang, “Architecture and optimization of high-throughput belief propagation decoding of polar codes,” in IEEE Int. Symp. Circuits Syst. (ISCAS), Montreal, Canada, May 2016, pp. 165-168. (Invited)
• [C33] T.-C. Ou, Z. Zhang, and M. Papaefthymiou, “A 934MHz 9Gb/s 3.2pJ/b/iteration charge-recovery LDPC decoder with in-package inductors,” in IEEE Asian Solid-State Circuits Conf. (A-SSCC), Xiamen, China, Nov. 2015.
• [C32] S. Lu, Z. Zhang, and M. Papaefthymiou, “1.32GHz high-throughput charge-recovery AES core with resistance to DPA attacks,” in Symp. VLSI Circuits, Kyoto, Japan, Jun. 2015, pp. 246-247.
• [C31] J. K. Kim, P. Knag, T. Chen, and Z. Zhang, “A 640M pixel/s 3.65mW sparse event-driven neuromorphic object recognition processor with on-chip learning,” in Symp. VLSI Circuits, Kyoto, Japan, Jun. 2015, pp. 50-51.
• [C30] C.-H. Chen, W. Tang, and Z. Zhang, “A 2.4mm2 130mW MMSE-nonbinary LDPC iterative detector-decoder for 4×4 256-QAM MIMO in 65nm CMOS,” in IEEE Int. Solid-State Circuits Conf. (ISSCC), San Francisco, CA, Feb. 2015, pp. 338-339.
• [C29] F. Sheikh, C.-H. Chen, D. Yoon, B. Alexandrov, K. Bowman, A. Chun, H. Alavi, and Z. Zhang, “3.2Gbps channel-adaptive configurable MIMO detector for multi-mode wireless communication,” in IEEE Workshop Signal Process. Syst. (SIPS), Belfast, UK, Oct. 2014.
• [C28] Y. S. Park, Y. Tao, S. Sun, and Z. Zhang, “A 4.68Gb/s belief propagation polar decoder with bit-splitting register file,” in Symp. VLSI Circuits, Honolulu, HI, Jun. 2014, pp. 117-118.
• [C27] J. K. Kim, P. Knag, T. Chen, and Z. Zhang, “A 6.67mW sparse coding ASIC enabling on-chip learning and inference,” in Symp. VLSI Circuits, Honolulu, HI, Jun. 2014, pp. 61-62.
• [C26] S. Gaba, P. Knag, Z. Zhang, and W. Lu, “Memristive devices for stochastic computing,” in IEEE Int. Symp. Circuits Syst. (ISCAS), Melbourne, Australia, Jun. 2014, pp. 2592-2595. (Invited)
• [C25] T.-C. Ou, Z. Zhang, and. M. C. Papaefthymiou, “An 821MHz 7.9Gb/s 7.3pJ/b/iteration charge-recovery LDPC decoder,” in IEEE Int. Solid-State Circuits Conf. (ISSCC), San Francisco, CA, Feb. 2014, pp. 462-463.
• [C24] D. Jeon, Y.-P. Chen, Y. Lee, Y. Kim, Z. Foo, G. Kruger, H. Oral, O. Berenfeld, Z. Zhang, D. Blaauw, and D. Sylvester, “An implantable 64nW ECG-monitoring mixed-signal SoC for arrhythmia diagnosis,” in IEEE Int. Solid-State Circuits Conf. (ISSCC), San Francisco, CA, Feb. 2014, pp. 416-417.
• [C23] C.-H. Chen, K. Bowman, C. Augustine, Z. Zhang, and J. Tschanz, “Minimum supply voltage for sequential logic circuits in a 22nm technology,” in IEEE Int. Symp. Low Power Electron. Des. (ISLPED), Beijing, China, Sep. 2013, pp. 181-186.
• [C22] D. Jeon, Y. Kim, I. Lee, Z. Zhang, D. Blaauw, and D. Sylvester, “A low-power VGA full-frame feature extraction processor,” in IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), Vancouver, Canada, May 2013, pp. 2726-2730.
• [C21] C.-H. Chen, Y. Tao, and Z. Zhang, “Efficient in situ error detection enabling diverse path coverage,” in IEEE Int. Symp. Circuits Syst. (ISCAS), Beijing, China, May 2013, pp. 773-776. (Best Student Paper Award Finalist)
• [C20] Y. S. Park, Y. Tao, and Z. Zhang, “A 1.15Gb/s fully parallel nonbinary LDPC decoder with fine-grained dynamic clock gating,” in IEEE Int. Solid-State Circuits Conf. (ISSCC), San Francisco, CA, Feb. 2013, pp. 422-423.
• [C19] D. Jeon, Y. Kim, I. Lee, Z. Zhang, D. Blaauw, and D. Sylvester, “A 470mV 2.7mW feature extraction accelerator for micro-autonomous vehicle navigation in 28nm CMOS,” in IEEE Int. Solid-State Circuits Conf. (ISSCC), San Francisco, CA, Feb. 2013, pp. 166-167.
• [C18] J. K. Kim, J. A. Fessler, and Z. Zhang, “Perburbation-based error analysis of iterative image reconstruction algorithm for X-ray computed tomography,” in Int. Conf. Image Formation in X-Ray Computed Tomography, Salt Lake City, UT, Jun. 2012, pp. 194-197.
• [C17] Y. S. Park, D. Blaauw, D. Sylvester, and Z. Zhang, “A 1.6-mm2 38-mW 1.5-Gb/s LDPC decoder enabled by refresh-free embedded DRAM,” in Symp. VLSI Circuits, Honolulu, HI, Jun. 2012, pp. 114-115.
• [C16] Y. Tao, Y. S. Park, and Z. Zhang, “High-throughput architecture and implementation of regular (2, dc) nonbinary LDPC decoders,” in IEEE Int. Symp. Circuits Syst. (ISCAS), Seoul, Korea, May 2012, pp. 2625-2628.
• [C15] H. Li, Y. S. Park, and Z. Zhang, “Reconfigurable architecture and automated design flow for rapid FPGA-based LDPC code emulation,” in ACM Int. Symp. Field-Programmable Gate Arrays (FPGA), Monterey, CA, Feb. 2012, pp. 167-170.
• [C14] J. Wang, L. Dolecek, Z. Zhang, and R. Wesel, “Absorbing set spectrum approach for practical code design,” in IEEE Int. Symp. Inf. Theory (ISIT), Saint Petersburg, Russia, Aug. 2011, pp. 2726-2730.
• [C13] J. K. Kim, Z. Zhang, and J. A. Fessler, “Hardware acceleration of iterative image reconstruction for X-ray computed tomography,” in IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), Prague, Czech Republic, May 2011, pp. 1697-1700.
• [C12] M. Weiner, B. Nikolic, and Z. Zhang, “LDPC decoder architecture for high-data rate personal-area networks,” in IEEE Int. Symp. Circuits Syst. (ISCAS), Rio de Janeiro, Brazil, May 2011, pp. 1784-1787. (Invited)
• [C11] C.-H. Chen, Y. Kim, Z. Zhang, D. Blaauw, D. Sylvester, H. Naeimi, and S. Sandhu, “A confidence-driven model for error-resilient computing,” in Design, Autom. Test in Europe Conf. (DATE), Grenoble, France, Mar. 2011.
• [C10] L. Dolecek, J. Wang, and Z. Zhang, “Towards improved LDPC code designs using absorbing set spectrum properties,” in Int. Symp. Turbo Codes Iterative Inform. Process. (ISTC), Brest, France, Sep. 2010, pp. 477-481.

Conference Proceedings (Prior to 2010)

• [C9] Z. Zhang, L. Dolecek, P. Lee, V. Anantharam, M. J. Wainwright, B. Richards, and B. Nikolic, “Low error rate LDPC decoders,” in Asilomar Conf. Signals, Syst., Comput., Pacific Grove, CA, Nov. 2009, pp. 1278-1282. (Invited)
• [C8] Z. Zhang, V. Anantharam, M. J. Wainwright, and B. Nikolic, “A 47 Gb/s LDPC decoder with improved low error rate performance,” in Symp. VLSI Circuits, Kyoto, Japan, Jun. 2009, pp. 286-287. (Best Student Paper Award)
• [C7] Z. Zhang, L. Dolecek, B. Nikolic, V. Anantharam, and M. J. Wainwright, “Lowering LDPC error floors by postprocessing,” in IEEE Global Commun. Conf. (GLOBECOM), New Orleans, LA, Nov. 2008.
• [C6] P. Lee, L. Dolecek, Z. Zhang, V. Anantharam, B. Nikolic, and M. Wainwright, “Error floors in LDPC codes: fast simulation, bounds and hardware emulation,” in IEEE Int. Symp. Inf. Theory (ISIT), Toronto, Canada, Jul. 2008, pp. 444-448.
• [C5] Z. Zhang, R. Winoto, A. Bahai, and B. Nikolic, “Peak-to-average power ratio reduction in an FDM broadcast system,” in IEEE Workshop Signal Process. Syst. (SIPS), Shanghai, China, Oct. 2007, pp. 25-30.
• [C4] L. Dolecek, Z. Zhang, M. Wainwright, V. Anantharam, and B. Nikolic, “Evaluation of the low frame error rate performance of LDPC codes using importance sampling,” in IEEE Inf. Theory Workshop (ITW), Tahoe City, CA, Sep. 2007, pp. 202-207.
• [C3] L. Dolecek, Z. Zhang, V. Anantharam, M. Wainwright, and B. Nikolic, “Analysis of absorbing sets for array-based LDPC codes,” in IEEE Int. Conf. Commun. (ICC), Glasgow, UK, Jun. 2007, pp. 6261-6268.
• [C2] Z. Zhang, L. Dolecek, M. Wainwright, V. Anantharam, and B. Nikolic, “Quantization effects in low-density parity-check decoders,” in IEEE Int. Conf. Commun. (ICC), Glasgow, UK, Jun. 2007, pp. 6231-6237.
• [C1] Z. Zhang, L. Dolecek, B. Nikolic, V. Anantharam, and M. Wainwright, “Investigation of error floors of structured low-density parity-check codes by hardware emulation,” in IEEE Global Commun. Conf. (GLOBECOM), San Francisco, CA, Nov. 2006. (Best Student Paper Award Finalist)

Book Chapters

• [B3] P. Knag, S. Gaba, W. Lu and Z. Zhang, “RRAM solutions for stochastic computing,” in Stochastic Computing: Techniques and Applications, W. Gross and V. C. Gaudet, Eds. Springer, 2019.
• [B2] S. Sun and Z. Zhang, “Design of high-performance error-correcting codes using FPGA,” in Reconfigurable Logic: Architecture, Tools and Applications, P.-E. Gaillardon, Ed. Boca Raton, FL: CRC Press, 2015.
• [B1] C.-H. Chen, P. Knag, and Z. Zhang, “Soft error resilient circuit design,” in VLSI: Circuits for Emerging Applications, T. Wojcicki, Ed. Boca Raton, FL: CRC Press, 2014.

Technical Reports

• [T2] Z. Zhang, “Design of LDPC decoders for improved low error rate performance,” Ph.D. dissertation, Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, 2009.
• [T1] Q. Zhu, Z. Zhang, A. Pinto, and A. L. Sangiovanni-Vincentelli, “On-chip networks modeling and simulation,” Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Technical Report EECS-2006-126, Oct. 2006.