294 citations · 898 across the 16 of their papers we have counts for
4 papers · 1 filter
Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference
Manuel Le Gallo, Corey Lammie, Julian Buechel +8
Analog In-Memory Computing (AIMC) is a promising approach to reduce the latency and energy consumption of Deep Neural Network (DNN) inference and training. However, the noisy and n…
AnalogNAS: A Neural Network Design Framework for Accurate Inference with Analog In-Memory Computing
Hadjer Benmeziane, Corey Lammie, Irem Boybat +9
The advancement of Deep Learning (DL) is driven by efficient Deep Neural Network (DNN) design and new hardware accelerators. Current DNN design is primarily tailored for general-pu…
Fast offset corrected in-memory training
Malte J. Rasch, Fabio Carta, Omebayode Fagbohungbe +1
In-memory computing with resistive crossbar arrays has been suggested to accelerate deep-learning workloads in highly efficient manner. To unleash the full potential of in-memory c…
Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators
Malte J. Rasch, Charles Mackin, Manuel Le Gallo +10
Analog in-memory computing (AIMC) -- a promising approach for energy-efficient acceleration of deep learning workloads -- computes matrix-vector multiplications (MVMs) but only app…