66 citations · 78 across the 6 of their papers we have counts for
6 papers
Robust Heterogeneous Analog-Digital Computing for Mixture-of-Experts Models with Theoretical Generalization Guarantees
Mohammed Nowaz Rabbani Chowdhury, Hsinyu Tsai, Geoffrey W. Burr +3
Sparse Mixture-of-Experts (MoE) models enable efficient scalability by activating only a small sub-set of experts per input, yet their massive parameter counts lead to substantial…
Rapid yet accurate Tile-circuit and device modeling for Analog In-Memory Computing
J. Luquin, C. Mackin, S. Ambrogio +11
Analog In-Memory Compute (AIMC) can improve the energy efficiency of Deep Learning by orders of magnitude. Yet analog-domain device and circuit non-idealities -- within the analog…
CiMBA: Accelerating Genome Sequencing through On-Device Basecalling via Compute-in-Memory
William Andrew Simon, Irem Boybat, Riselda Kodra +8
As genome sequencing is finding utility in a wide variety of domains beyond the confines of traditional medical settings, its computational pipeline faces two significant challenge…
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…
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…