Enabling resource-efficient aiot system with cross-level optimization: A survey
The emerging field of artificial intelligence of things (AIoT, AI+ IoT) is driven by the
widespread use of intelligent infrastructures and the impressive success of deep learning …
widespread use of intelligent infrastructures and the impressive success of deep learning …
Hardware and software optimizations for accelerating deep neural networks: Survey of current trends, challenges, and the road ahead
Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning
(DL) is already present in many applications ranging from computer vision for medicine to …
(DL) is already present in many applications ranging from computer vision for medicine to …
Gemmini: Enabling systematic deep-learning architecture evaluation via full-stack integration
DNN accelerators are often developed and evaluated in isolation without considering the
cross-stack, system-level effects in real-world environments. This makes it difficult to …
cross-stack, system-level effects in real-world environments. This makes it difficult to …
ZigZag: Enlarging joint architecture-map** design space exploration for DNN accelerators
Building efficient embedded deep learning systems requires a tight co-design between DNN
algorithms, hardware, and algorithm-to-hardware map**, aka dataflow. However, owing to …
algorithms, hardware, and algorithm-to-hardware map**, aka dataflow. However, owing to …
Sparseloop: An analytical approach to sparse tensor accelerator modeling
In recent years, many accelerators have been proposed to efficiently process sparse tensor
algebra applications (eg, sparse neural networks). However, these proposals are single …
algebra applications (eg, sparse neural networks). However, these proposals are single …
Gpt4aigchip: Towards next-generation ai accelerator design automation via large language models
The remarkable capabilities and intricate nature of Artificial Intelligence (AI) have
dramatically escalated the imperative for specialized AI accelerators. Nonetheless …
dramatically escalated the imperative for specialized AI accelerators. Nonetheless …
AutoDNNchip: An automated DNN chip predictor and builder for both FPGAs and ASICs
Recent breakthroughs in Deep Neural Networks (DNNs) have fueled a growing demand for
domain-specific hardware accelerators (ie, DNN chips). However, designing DNN chips is …
domain-specific hardware accelerators (ie, DNN chips). However, designing DNN chips is …
Review of ASIC accelerators for deep neural network
Deep neural networks (DNNs) have become an essential tool in artificial intelligence, with a
wide range of applications such as computer vision, medical diagnosis, security, robotics …
wide range of applications such as computer vision, medical diagnosis, security, robotics …
Hardware acceleration of sparse and irregular tensor computations of ml models: A survey and insights
Machine learning (ML) models are widely used in many important domains. For efficiently
processing these computational-and memory-intensive applications, tensors of these …
processing these computational-and memory-intensive applications, tensors of these …
A 0.32–128 TOPS, scalable multi-chip-module-based deep neural network inference accelerator with ground-referenced signaling in 16 nm
Custom accelerators improve the energy efficiency, area efficiency, and performance of
deep neural network (DNN) inference. This article presents a scalable DNN accelerator …
deep neural network (DNN) inference. This article presents a scalable DNN accelerator …