Rasch, Malte J.

A Kernel Method for the Two-Sample-Problem

Gretton, A., Borgwardt, K. M., Rasch, M., Schölkopf, B., & Smola, A. J. (2007) · Advances in Neural Information Processing Systems 19

Probability and Statistical ResearchStatistical Methods and Applications

Cited by 2,167

Integrating structured biological data by Kernel Maximum Mean Discrepancy

Borgwardt, K. M., Gretton, A., Rasch, M. J., Kriegel, H.-P., Schölkopf, B., & Smola, A. J. (2006) · Bioinformatics

Gene expression and cancer classificationBioinformatics and Genomic Networks

Cited by 1,674

Distinct inhibitory circuits orchestrate cortical beta and gamma band oscillations

Chen, G., Zhang, Y., Li, X., Zhao, X., Ye, Q., Lin, Y., Tao, H. W., Rasch, M. J., & Zhang, X. (2017) · Neuron

Neural dynamics and brain functionNeuroscience and Neuropharmacology Research

Cited by 345

A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference

Le Gallo, M., Khaddam-Aljameh, R., Stanisavljevic, M., Vasilopoulos, A., Kersting, B., Dazzi, M., Karunaratne, G., Brändli, M., Singh, A., Müller, S. M., et al. (2023) · Nature Electronics

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

Cited by 310

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

A Flexible and Fast PyTorch Toolkit for Simulating Training and Inference on Analog Crossbar Arrays

Rasch, M. J., Moreda, D., Gokmen, T., Le Gallo, M., Carta, F., Goldberg, C., El Maghraoui, K., Sebastian, A., & Narayanan, V. (2021) · 2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS)

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 160

On-Chip Trainable 1.4M 6T2R PCM Synaptic Array with 1.6K Stochastic LIF Neurons for Spiking RBM

Ishii, M., Shin, U., Hosokawa, K., BrightSky, M., Haensch, W., Kim, S., Lewis, S., Okazaki, A., Okazawa, J., Ito, M., et al. (2019) · 2019 IEEE International Electron Devices Meeting (IEDM)

Advanced Memory and Neural ComputingAnalog and Mixed-Signal Circuit Design

Cited by 43

The marriage of training and inference for scaled deep learning analog hardware

Gokmen, T., Rasch, M. J., & Haensch, W. (2019) · 2019 IEEE International Electron Devices Meeting (IEDM)

Advanced Memory and Neural ComputingAnalog and Mixed-Signal Circuit Design

Cited by 37

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

RAPA-ConvNets: Modified Convolutional Networks for Accelerated Training on Architectures With Analog Arrays

Rasch, M. J., Gokmen, T., Rigotti, M., & Haensch, W. (2019) · Frontiers in Neuroscience

Advanced Neural Network ApplicationsAdvanced Memory and Neural Computing

Cited by 14