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ORCIDMalte J. Rasch
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2020 – today
- 2024
[c14]Kaoutar El Maghraoui, Kim Tran, Kurtis Ruby, Borja Godoy, Jordan Murray, Manuel Le Gallo-Bourdeau, Todd Deshane, Pablo Gonzalez, Diego Moreda, Hadjer Benmeziane, Corey Liam Lammie, Julian Büchel, Malte J. Rasch, Abu Sebastian, Vijay Narayanan:
Analog AI as a Service: A Cloud Platform for In-Memory Computing. SSE 2024: 43-53- [c13]Corey Lammie, Athanasios Vasilopoulos, Julian Büchel, Giacomo Camposampiero, Manuel Le Gallo, Malte J. Rasch, Abu Sebastian:
Improving the Accuracy of Analog-Based In-Memory Computing Accelerators Post-Training. ISCAS 2024: 1-5 - [c12]H. Y. Cheng, Z. L. Liu, A. Majumdar, Alexander Grun, A. Ray, J. Su, Malte J. Rasch, Fabio Carta, Lynne M. Gignac, C. Lavoie, C. W. Cheng, M. Bright Sky, H. L. Lung:
State-Independent Low Resistance Drift SiSbTe Phase Change Memory for Analog In-Memory Computing Applications. VLSI Technology and Circuits 2024: 1-2 - [i13]Corey Lammie, Athanasios Vasilopoulos, Julian Büchel, Giacomo Camposampiero, Manuel Le Gallo, Malte J. Rasch, Abu Sebastian:
Improving the Accuracy of Analog-Based In-Memory Computing Accelerators Post-Training. CoRR abs/2401.09859 (2024) - [i12]Vassilis Kalantzis, Mark S. Squillante, Shashanka Ubaru, Tayfun Gokmen, Chai Wah Wu, Anshul Gupta, Haim Avron, Tomasz Nowicki, Malte J. Rasch, O. Murat Onen, Vanessa López-Marrero
, Effendi Leobandung, Yasuteru Kohda, Wilfried Haensch, Lior Horesh:
Multi-Function Multi-Way Analog Technology for Sustainable Machine Intelligence Computation. CoRR abs/2401.13754 (2024) - [i11]Zhaoxian Wu, Tayfun Gokmen, Malte J. Rasch, Tianyi Chen:
Towards Exact Gradient-based Training on Analog In-memory Computing. CoRR abs/2406.12774 (2024) - 2023
- [c11]Hadjer Benmeziane, Corey Lammie, Irem Boybat, Malte J. Rasch, Manuel Le Gallo, Hsinyu Tsai, Ramachandran Muralidhar, Smaïl Niar, Hamza Ouarnoughi, Vijay Narayanan, Abu Sebastian, Kaoutar El Maghraoui:
AnalogNAS: A Neural Network Design Framework for Accurate Inference with Analog In-Memory Computing. EDGE 2023: 233-244 - [c10]Martin M. Frank, Ning Li, Malte J. Rasch, Shubham Jain, Ching-Tzu Chen, Ramachandran Muralidhar, Jin-Ping Han, Vijay Narayanan, Timothy Philip, Kevin Brew, Andrew Simon, Iqbal Saraf, Nicole Saulnier, Irem Boybat, Stanislaw Wozniak, Abu Sebastian, Pritish Narayanan, Charles Mackin, An Chen, Hsinyu Tsai, Geoffrey W. Burr:
Impact of Phase-Change Memory Drift on Energy Efficiency and Accuracy of Analog Compute-in-Memory Deep Learning Inference (Invited). IRPS 2023: 1-10 - [c9]Hsinyu Tsai, Pritish Narayanan, Shubham Jain, Stefano Ambrogio, Kohji Hosokawa, Masatoshi Ishii, Charles Mackin, Ching-Tzu Chen, Atsuya Okazaki, Akiyo Nomura, Irem Boybat, Ramachandran Muralidhar, Martin M. Frank, Takeo Yasuda, Alexander M. Friz, Yasuteru Kohda, An Chen, Andrea Fasoli, Malte J. Rasch, Stanislaw Wozniak, Jose Luquin, Vijay Narayanan, Geoffrey W. Burr:
Architectures and Circuits for Analog-memory-based Hardware Accelerators for Deep Neural Networks (Invited). ISCAS 2023: 1-5 - [i10]Malte J. Rasch, Charles Mackin, Manuel Le Gallo, An Chen, Andrea Fasoli, Frédéric Odermatt, Ning Li, S. R. Nandakumar, Pritish Narayanan, Hsinyu Tsai, Geoffrey W. Burr, Abu Sebastian, Vijay Narayanan:
Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators. CoRR abs/2302.08469 (2023) - [i9]Malte J. Rasch, Fabio Carta, Omobayode Fagbohungbe, Tayfun Gokmen:
Fast offset corrected in-memory training. CoRR abs/2303.04721 (2023) - [i8]Hadjer Benmeziane, Corey Lammie, Irem Boybat, Malte J. Rasch, Manuel Le Gallo, Hsinyu Tsai, Ramachandran Muralidhar, Smaïl Niar, Hamza Ouarnoughi, Vijay Narayanan, Abu Sebastian, Kaoutar El Maghraoui:
AnalogNAS: A Neural Network Design Framework for Accurate Inference with Analog In-Memory Computing. CoRR abs/2305.10459 (2023) - [i7]Manuel Le Gallo, Corey Lammie, Julian Büchel, Fabio Carta, Omobayode Fagbohungbe, Charles Mackin, Hsinyu Tsai, Vijay Narayanan, Abu Sebastian, Kaoutar El Maghraoui, Malte J. Rasch:
Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference. CoRR abs/2307.09357 (2023) - 2022
- [j9]Jin-Ping Han, Malte J. Rasch, Zuoguang Liu, Paul Solomon, Kevin Brew, Kangguo Cheng, Injo Ok, Victor Chan, Michael Longstreet, Wanki Kim, Robert L. Bruce, Cheng-Wei Cheng, Nicole Saulnier, Vijay Narayanan:
Impact of Phase-Change Memory Flicker Noise and Weight Drift on Analog Hardware Inference for Large-Scale Deep Learning Networks. Adv. Intell. Syst. 4(5) (2022) - [j8]Uicheol Shin, Masatoshi Ishii, Atsuya Okazaki, Megumi Ito, Malte J. Rasch, Wanki Kim, Akiyo Nomura, Wonseok Choi, Dooyong Koh, Kohji Hosokawa, Matthew BrightSky, Seiji Munetoh, SangBum Kim:
Pattern Training, Inference, and Regeneration Demonstration Using On-Chip Trainable Neuromorphic Chips for Spiking Restricted Boltzmann Machine. Adv. Intell. Syst. 4(8) (2022) - [c8]Atsuya Okazaki, Pritish Narayanan, Stefano Ambrogio, Kohji Hosokawa, Hsinyu Tsai, Akiyo Nomura, Takeo Yasuda, Charles Mackin, Alexander M. Friz, Masatoshi Ishii, Yasuteru Kohda, Katie Spoon, An Chen, Andrea Fasoli, Malte J. Rasch, Geoffrey W. Burr:
Analog-memory-based 14nm Hardware Accelerator for Dense Deep Neural Networks including Transformers. ISCAS 2022: 3319-3323 - 2021
- [j7]Katie Spoon, Hsinyu Tsai, An Chen, Malte J. Rasch, Stefano Ambrogio, Charles Mackin, Andrea Fasoli, Alexander M. Friz, Pritish Narayanan, Milos Stanisavljevic, Geoffrey W. Burr:
Toward Software-Equivalent Accuracy on Transformer-Based Deep Neural Networks With Analog Memory Devices. Frontiers Comput. Neurosci. 15: 675741 (2021) - [c7]Malte J. Rasch, Diego Moreda, Tayfun Gokmen, Manuel Le Gallo, Fabio Carta, Cindy Goldberg, Kaoutar El Maghraoui, Abu Sebastian, Vijay Narayanan:
A Flexible and Fast PyTorch Toolkit for Simulating Training and Inference on Analog Crossbar Arrays. AICAS 2021: 1-4 - [i6]Malte J. Rasch, Diego Moreda, Tayfun Gokmen, Manuel Le Gallo, Fabio Carta, Cindy Goldberg, Kaoutar El Maghraoui, Abu Sebastian, Vijay Narayanan:
A flexible and fast PyTorch toolkit for simulating training and inference on analog crossbar arrays. CoRR abs/2104.02184 (2021) - 2020
- [j6]Malte J. Rasch, Tayfun Gokmen, Wilfried Haensch:
Training Large-scale Artificial Neural Networks on Simulated Resistive Crossbar Arrays. IEEE Des. Test 37(2): 19-29 (2020) - [c6]Effendi Leobandung, Malte J. Rasch, Y. Li:
Synchronized Analog Capacitor Arrays for Parallel Convolutional Neural Network Training. MWSCAS 2020: 387-390
2010 – 2019
- 2019
- [j5]Shubham Jain, Aayush Ankit, Indranil Chakraborty, Tayfun Gokmen, Malte J. Rasch, Wilfried Haensch, Kaushik Roy, Anand Raghunathan:
Neural network accelerator design with resistive crossbars: Opportunities and challenges. IBM J. Res. Dev. 63(6): 10:1-10:13 (2019) - [c5]Megumi Ito, Malte J. Rasch, Masatoshi Ishii, Atsuya Okazaki, SangBum Kim, Junka Okazawa, Akiyo Nomura, Kohji Hosokawa, Wilfried Haensch:
Training Large-Scale Spiking Neural Networks on Multi-core Neuromorphic System Using Backpropagation. ICONIP (3) 2019: 185-194 - [i5]Malte J. Rasch, Tayfun Gokmen, Wilfried Haensch:
Training large-scale ANNs on simulated resistive crossbar arrays. CoRR abs/1906.02698 (2019) - [i4]Hyungjun Kim, Malte J. Rasch, Tayfun Gokmen, Takashi Ando, Hiroyuki Miyazoe, Jae-Joon Kim, John Rozen, Seyoung Kim:
Zero-shifting Technique for Deep Neural Network Training on Resistive Cross-point Arrays. CoRR abs/1907.10228 (2019) - 2018
- [i3]Tayfun Gokmen, Malte J. Rasch, Wilfried Haensch:
Training LSTM Networks with Resistive Cross-Point Devices. CoRR abs/1806.00166 (2018) - [i2]Malte J. Rasch, Tayfun Gokmen, Mattia Rigotti, Wilfried Haensch:
Efficient ConvNets for Analog Arrays. CoRR abs/1807.01356 (2018) - 2015
- [j4]Tao Wang, Luping Yin, Xiaolong Zou, Yousheng Shu, Malte J. Rasch, Si Wu:
A Phenomenological Synapse Model for Asynchronous Neurotransmitter Release. Frontiers Comput. Neurosci. 9: 153 (2015) - 2013
- [j3]Danke Zhang, Yuanqing Li, Malte J. Rasch, Si Wu:
Nonlinear multiplicative dendritic integration in neuron and network models. Frontiers Comput. Neurosci. 7: 56 (2013) - [j2]Danke Zhang, Yuanqing Li, Si Wu, Malte J. Rasch:
Design principles of the sparse coding network and the role of "sister cells" in the olfactory system of Drosophila. Frontiers Comput. Neurosci. 7: 141 (2013) - 2012
- [j1]Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola:
A Kernel Two-Sample Test. J. Mach. Learn. Res. 13: 723-773 (2012) - 2011
- [c4]Libo Ma, Malte J. Rasch, Si Wu:
Learning Variance Statistics of Natural Images. ISNN (2) 2011: 429-436
2000 – 2009
- 2008
- [i1]Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola:
A Kernel Method for the Two-Sample Problem. CoRR abs/0805.2368 (2008) - 2007
- [c3]Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola:
A Kernel Approach to Comparing Distributions. AAAI 2007: 1637-1641 - 2006
- [c2]Karsten M. Borgwardt, Arthur Gretton, Malte J. Rasch, Hans-Peter Kriegel, Bernhard Schölkopf, Alexander J. Smola:
Integrating structured biological data by Kernel Maximum Mean Discrepancy. ISMB (Supplement of Bioinformatics) 2006: 49-57 - [c1]Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola:
A Kernel Method for the Two-Sample-Problem. NIPS 2006: 513-520
Coauthor Index
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last updated on 2024-10-18 19:30 CEST by the dblp team
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