Efficient deep learning: A survey on making deep learning models smaller, faster, and better
G Menghani - ACM Computing Surveys, 2023 - dl.acm.org
Deep learning has revolutionized the fields of computer vision, natural language
understanding, speech recognition, information retrieval, and more. However, with the …
understanding, speech recognition, information retrieval, and more. However, with the …
Deep learning in electron microscopy
JM Ede - Machine Learning: Science and Technology, 2021 - iopscience.iop.org
Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …
microscopy. This review paper offers a practical perspective aimed at developers with …
Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
In deep learning, models typically reuse the same parameters for all inputs. Mixture of
Experts (MoE) models defy this and instead select different parameters for each incoming …
Experts (MoE) models defy this and instead select different parameters for each incoming …
Hardware architecture and software stack for PIM based on commercial DRAM technology: Industrial product
Emerging applications such as deep neural network demand high off-chip memory
bandwidth. However, under stringent physical constraints of chip packages and system …
bandwidth. However, under stringent physical constraints of chip packages and system …
Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators
Analog in-memory computing—a promising approach for energy-efficient acceleration of
deep learning workloads—computes matrix-vector multiplications but only approximately …
deep learning workloads—computes matrix-vector multiplications but only approximately …
Mixed precision algorithms in numerical linear algebra
Today's floating-point arithmetic landscape is broader than ever. While scientific computing
has traditionally used single precision and double precision floating-point arithmetics, half …
has traditionally used single precision and double precision floating-point arithmetics, half …
Pushing the limits of narrow precision inferencing at cloud scale with microsoft floating point
B Darvish Rouhani, D Lo, R Zhao… - Advances in neural …, 2020 - proceedings.neurips.cc
In this paper, we explore the limits of Microsoft Floating Point (MSFP), a new class of
datatypes developed for production cloud-scale inferencing on custom hardware. Through …
datatypes developed for production cloud-scale inferencing on custom hardware. Through …
1.1 the deep learning revolution and its implications for computer architecture and chip design
J Dean - 2020 IEEE International Solid-State Circuits …, 2020 - ieeexplore.ieee.org
The past decade has seen a remarkable series of advances in machine learning, and in
particular deeplearning approaches based on artificial neural networks, to improve our …
particular deeplearning approaches based on artificial neural networks, to improve our …
[HTML][HTML] Applications of deep learning in fish habitat monitoring: A tutorial and survey
Marine ecosystems and their fish habitats are becoming increasingly important due to their
integral role in providing a valuable food source and conservation outcomes. Due to their …
integral role in providing a valuable food source and conservation outcomes. Due to their …
Tensordash: Exploiting sparsity to accelerate deep neural network training
TensorDash is a hardware-based technique that enables data-parallel MAC units to take
advantage of sparsity in their input operand streams. When used to compose a hardware …
advantage of sparsity in their input operand streams. When used to compose a hardware …