Mackin, C.

An analog-AI chip for energy-efficient speech recognition and transcription

Ambrogio, S., Narayanan, P., Okazaki, A., Fasoli, A., Mackin, C., Hosokawa, K., Nomura, A., Yasuda, T., Chen, A., Friz, A., et al. (2023) · Nature

Advanced Memory and Neural ComputingLow-power high-performance VLSI design

Cited by 269

Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators

Rasch, M. J., Mackin, C., Le Gallo, M., Chen, A., Fasoli, A., Odermatt, F., Li, N., Nandakumar, S. R., Narayanan, P., Tsai, H., et al. (2023) · Nature Communications

Advanced Memory and Neural ComputingAdvanced Neural Network Applications

Cited by 213

Fully On-Chip MAC at 14 nm Enabled by Accurate Row-Wise Programming of PCM-Based Weights and Parallel Vector-Transport in Duration-Format

Narayanan, P., Ambrogio, S., Okazaki, A., Hosokawa, K., Tsai, H., Nomura, A., Yasuda, T., Mackin, C., Lewis, S. C., Friz, A., et al. (2021) · IEEE Transactions on Electron Devices

Advanced Memory and Neural ComputingLow-power high-performance VLSI design

Cited by 85

Reducing the Impact of Phase-Change Memory Conductance Drift on the Inference of large-scale Hardware Neural Networks

Ambrogio, S., Gallot, M., Spoon, K., Tsai, H., Mackin, C., Wesson, M., Kariyappa, S., Narayanan, P., Liu, C.-C., Kumar, A., et al. (2019) · 2019 IEEE International Electron Devices Meeting (IEDM)

Advanced Memory and Neural ComputingPhase-change materials and chalcogenides

Cited by 76

AI hardware acceleration with analog memory: Microarchitectures for low energy at high speed

Chang, H.-Y., Narayanan, P., Lewis, S. C., Farinha, N. C. P., Hosokawa, K., Mackin, C., Tsai, H., Ambrogio, S., Chen, A., & Burr, G. W. (2019) · IBM Journal of Research and Development

Advanced Memory and Neural ComputingAnalog and Mixed-Signal Circuit Design

Cited by 62

Inference of Long-Short Term Memory networks at software-equivalent accuracy using 2.5M analog Phase Change Memory devices

Tsai, H., Ambrogio, S., Mackin, C., Narayanan, P., Shelby, R. M., Rocki, K., Chen, A., & Burr, G. W. (2019) · 2019 Symposium on VLSI Technology

Advanced Memory and Neural ComputingPhase-change materials and chalcogenides

Cited by 47

Noise-Resilient DNN: Tolerating Noise in PCM-Based AI Accelerators via Noise-Aware Training

Kariyappa, S., Tsai, H., Spoon, K., Ambrogio, S., Narayanan, P., Mackin, C., Chen, A., Qureshi, M., & Burr, G. W. (2021) · IEEE Transactions on Electron Devices

Advanced Memory and Neural ComputingLow-power high-performance VLSI design

Cited by 45

Weight Programming in DNN Analog Hardware Accelerators in the Presence of NVM Variability

Mackin, C., Tsai, H., Ambrogio, S., Narayanan, P., Chen, A., & Burr, G. W. (2019) · Advanced Electronic Materials

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 41

Toward Software-Equivalent Accuracy on Transformer-Based Deep Neural Networks With Analog Memory Devices

Spoon, K., Tsai, H., Chen, A., Rasch, M. J., Ambrogio, S., Mackin, C., Fasoli, A., Friz, A. M., Narayanan, P., Stanisavljevic, M., et al. (2021) · Frontiers in Computational Neuroscience

Advanced Memory and Neural ComputingAdvanced Neural Network Applications

Cited by 26

Mushroom-Type phase change memory with projection liner: An array-level demonstration of conductance drift and noise mitigation

Bruce, R. L., Sarwat, S. G., Boybat, I., Cheng, C.-W., Kim, W., Nandakumar, S. R., Mackin, C., Philip, T., Liu, Z., Brew, K., et al. (2021) · 2021 IEEE International Reliability Physics Symposium (IRPS)

Advanced Memory and Neural ComputingPhase-change materials and chalcogenides

Cited by 23