294 citations · 370 across the 8 of their papers we have counts for
8 papers
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…
A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference
Manuel Le Gallo, Riduan Khaddam-Aljameh, Milos Stanisavljevic +26
The need to repeatedly shuttle around synaptic weight values from memory to processing units has been a key source of energy inefficiency associated with hardware implementation of…
ALBench: A Framework for Evaluating Active Learning in Object Detection
Zhanpeng Feng, Shiliang Zhang, Rinyoichi Takezoe +5
Active learning is an important technology for automated machine learning systems. In contrast to Neural Architecture Search (NAS) which aims at automating neural network architect…
Joint Coreset Construction and Quantization for Distributed Machine Learning
Hanlin Lu, Changchang Liu, Shiqiang Wang +4
Coresets are small, weighted summaries of larger datasets, aiming at providing provable error bounds for machine learning (ML) tasks while significantly reducing the communication…
Robust Coreset Construction for Distributed Machine Learning
Hanlin Lu, Ming-Ju Li, Ting He +3
Coreset, which is a summary of the original dataset in the form of a small weighted set in the same sample space, provides a promising approach to enable machine learning over dist…
A Quantitative Evaluation Framework for Missing Value Imputation Algorithms
Vinod Nair, Rahul Kidambi, Sundararajan Sellamanickam +3
We consider the problem of quantitatively evaluating missing value imputation algorithms. Given a dataset with missing values and a choice of several imputation algorithms to fill…