Malte J. Rasch

41 papers A* 2C 4Journal 25Unranked 10

Year	Rank	Type	Title / Venue / Authors
2025	C	`conf`	Assessing the Performance of Analog Training for Transfer Learning. ISCAS Omobayode Fagbohungbe, Corey Lammie, Malte J. Rasch, Takashi Ando, Tayfun Gokmen, Vijay Narayanan
2025	J	`jnl`	Assessing the Performance of Analog Training for Transfer Learning. CoRR Omobayode Fagbohungbe, Corey Lammie, Malte J. Rasch, Takashi Ando, Tayfun Gokmen, Vijay Narayanan
2025	J	`jnl`	Assessing the Performance of Analog Training for Transfer Learning. IEEE Trans. Circuits Syst. II Express Briefs Omobayode Fagbohungbe, Corey Lammie, Malte J. Rasch, Takashi Ando, Tayfun Gokmen, Vijay Narayanan
2025	J	`jnl`	Rapid yet accurate Tile-circuit and device modeling for Analog In-Memory Computing. CoRR Jose Luquin, Charles Mackin, Stefano Ambrogio, An Chen, F. Baldi, G. Miralles, Malte J. Rasch, Julian Büchel, M. Lalwani, W. Ponghiran, Paul Solomon, Hsinyu Tsai, Geoffrey W. Burr, Pritish Narayanan
2024	—	`conf`	Analog AI as a Service: A Cloud Platform for In-Memory Computing. SSE 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
2024	C	`conf`	Improving the Accuracy of Analog-Based In-Memory Computing Accelerators Post-Training. ISCAS Corey Lammie, Athanasios Vasilopoulos, Julian Büchel, Giacomo Camposampiero, Manuel Le Gallo, Malte J. Rasch, Abu Sebastian
2024	J	`jnl`	Improving the Accuracy of Analog-Based In-Memory Computing Accelerators Post-Training. CoRR Corey Lammie, Athanasios Vasilopoulos, Julian Büchel, Giacomo Camposampiero, Manuel Le Gallo, Malte J. Rasch, Abu Sebastian
2024	J	`jnl`	Multi-Function Multi-Way Analog Technology for Sustainable Machine Intelligence Computation. CoRR 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
2024	—	`conf`	State-Independent Low Resistance Drift SiSbTe Phase Change Memory for Analog In-Memory Computing Applications. VLSI Technology and Circuits Huai-Yu Cheng, Zhi-Lun Liu, A. Majumdar, Alexander Grun, A. Ray, J. Su, Malte J. Rasch, Fabio Carta, Lynne M. Gignac, Christian Lavoie, C. W. Cheng, M. Bright Sky, H. L. Lung
2024	A*	`conf`	Towards Exact Gradient-based Training on Analog In-memory Computing. NeurIPS Zhaoxian Wu, Tayfun Gokmen, Malte J. Rasch, Tianyi Chen
2024	J	`jnl`	Towards Exact Gradient-based Training on Analog In-memory Computing. CoRR Zhaoxian Wu, Tayfun Gokmen, Malte J. Rasch, Tianyi Chen
2023	—	`conf`	AnalogNAS: A Neural Network Design Framework for Accurate Inference with Analog In-Memory Computing. EDGE 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
2023	J	`jnl`	AnalogNAS: A Neural Network Design Framework for Accurate Inference with Analog In-Memory Computing. CoRR 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
2023	C	`conf`	Architectures and Circuits for Analog-memory-based Hardware Accelerators for Deep Neural Networks (Invited). ISCAS 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
2023	J	`jnl`	Fast offset corrected in-memory training. CoRR Malte J. Rasch, Fabio Carta, Omobayode Fagbohungbe, Tayfun Gokmen
2023	J	`jnl`	Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators. CoRR 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
2023	—	`conf`	Impact of Phase-Change Memory Drift on Energy Efficiency and Accuracy of Analog Compute-in-Memory Deep Learning Inference (Invited). IRPS 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
2023	J	`jnl`	Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference. CoRR 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
2022	C	`conf`	Analog-memory-based 14nm Hardware Accelerator for Dense Deep Neural Networks including Transformers. ISCAS 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
2022	J	`jnl`	Impact of Phase-Change Memory Flicker Noise and Weight Drift on Analog Hardware Inference for Large-Scale Deep Learning Networks. Adv. Intell. Syst. 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
2022	J	`jnl`	Pattern Training, Inference, and Regeneration Demonstration Using On-Chip Trainable Neuromorphic Chips for Spiking Restricted Boltzmann Machine. Adv. Intell. Syst. 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
2021	—	`conf`	A Flexible and Fast PyTorch Toolkit for Simulating Training and Inference on Analog Crossbar Arrays. AICAS Malte J. Rasch, Diego Moreda, Tayfun Gokmen, Manuel Le Gallo, Fabio Carta, Cindy Goldberg, Kaoutar El Maghraoui, Abu Sebastian, Vijay Narayanan
2021	J	`jnl`	A flexible and fast PyTorch toolkit for simulating training and inference on analog crossbar arrays. CoRR Malte J. Rasch, Diego Moreda, Tayfun Gokmen, Manuel Le Gallo, Fabio Carta, Cindy Goldberg, Kaoutar El Maghraoui, Abu Sebastian, Vijay Narayanan
2021	J	`jnl`	Toward Software-Equivalent Accuracy on Transformer-Based Deep Neural Networks With Analog Memory Devices. Frontiers Comput. Neurosci. 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
2020	—	`conf`	Synchronized Analog Capacitor Arrays for Parallel Convolutional Neural Network Training. MWSCAS Effendi Leobandung, Malte J. Rasch, Y. Li
2020	J	`jnl`	Training Large-scale Artificial Neural Networks on Simulated Resistive Crossbar Arrays. IEEE Des. Test Malte J. Rasch, Tayfun Gokmen, Wilfried Haensch
2019	J	`jnl`	Neural network accelerator design with resistive crossbars: Opportunities and challenges. IBM J. Res. Dev. Shubham Jain, Aayush Ankit, Indranil Chakraborty, Tayfun Gokmen, Malte J. Rasch, Wilfried Haensch, Kaushik Roy, Anand Raghunathan
2019	—	`conf`	Training Large-Scale Spiking Neural Networks on Multi-core Neuromorphic System Using Backpropagation. ICONIP (3) Megumi Ito, Malte J. Rasch, Masatoshi Ishii, Atsuya Okazaki, Sangbum Kim, Junka Okazawa, Akiyo Nomura, Kohji Hosokawa, Wilfried Haensch
2019	J	`jnl`	Training large-scale ANNs on simulated resistive crossbar arrays. CoRR Malte J. Rasch, Tayfun Gokmen, Wilfried Haensch
2019	J	`jnl`	Zero-shifting Technique for Deep Neural Network Training on Resistive Cross-point Arrays. CoRR Hyungjun Kim, Malte J. Rasch, Tayfun Gokmen, Takashi Ando, Hiroyuki Miyazoe, Jae-Joon Kim, John Rozen, Seyoung Kim
2018	J	`jnl`	Efficient ConvNets for Analog Arrays. CoRR Malte J. Rasch, Tayfun Gokmen, Mattia Rigotti, Wilfried Haensch
2018	J	`jnl`	Training LSTM Networks with Resistive Cross-Point Devices. CoRR Tayfun Gokmen, Malte J. Rasch, Wilfried Haensch
2015	J	`jnl`	A Phenomenological Synapse Model for Asynchronous Neurotransmitter Release. Frontiers Comput. Neurosci. Tao Wang, Luping Yin, Xiaolong Zou, Yousheng Shu, Malte J. Rasch, Si Wu
2013	J	`jnl`	Design principles of the sparse coding network and the role of "sister cells" in the olfactory system of Drosophila. Frontiers Comput. Neurosci. Danke Zhang, Yuanqing Li, Si Wu, Malte J. Rasch
2013	J	`jnl`	Nonlinear multiplicative dendritic integration in neuron and network models. Frontiers Comput. Neurosci. Danke Zhang, Yuanqing Li, Malte J. Rasch, Si Wu
2012	J	`jnl`	A Kernel Two-Sample Test. J. Mach. Learn. Res. Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola
2011	—	`conf`	Learning Variance Statistics of Natural Images. ISNN (2) Libo Ma, Malte J. Rasch, Si Wu
2008	J	`jnl`	A Kernel Method for the Two-Sample Problem CoRR Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola
2007	A*	`conf`	A Kernel Approach to Comparing Distributions. AAAI Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola
2006	—	`conf`	A Kernel Method for the Two-Sample-Problem. NIPS Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola
2006	—	`conf`	Integrating structured biological data by Kernel Maximum Mean Discrepancy. ISMB (Supplement of Bioinformatics) Karsten M. Borgwardt, Arthur Gretton, Malte J. Rasch, Hans-Peter Kriegel, Bernhard Schölkopf, Alexander J. Smola