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A survey on federated learning for resource-constrained IoT devices
Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …
model by learning from multiple decentralized edge clients. FL enables on-device training …
A survey on efficient convolutional neural networks and hardware acceleration
Over the past decade, deep-learning-based representations have demonstrated remarkable
performance in academia and industry. The learning capability of convolutional neural …
performance in academia and industry. The learning capability of convolutional neural …
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 …
Sigma: A sparse and irregular gemm accelerator with flexible interconnects for dnn training
The advent of Deep Learning (DL) has radically transformed the computing industry across
the entire spectrum from algorithms to circuits. As myriad application domains embrace DL, it …
the entire spectrum from algorithms to circuits. As myriad application domains embrace DL, it …
Machine learning at facebook: Understanding inference at the edge
At Facebook, machine learning provides a wide range of capabilities that drive many
aspects of user experience including ranking posts, content understanding, object detection …
aspects of user experience including ranking posts, content understanding, object detection …
Benchmarking a new paradigm: Experimental analysis and characterization of a real processing-in-memory system
Many modern workloads, such as neural networks, databases, and graph processing, are
fundamentally memory-bound. For such workloads, the data movement between main …
fundamentally memory-bound. For such workloads, the data movement between main …
A configurable cloud-scale DNN processor for real-time AI
Interactive AI-powered services require low-latency evaluation of deep neural network
(DNN) models-aka"" real-time AI"". The growing demand for computationally expensive …
(DNN) models-aka"" real-time AI"". The growing demand for computationally expensive …
In-datacenter performance analysis of a tensor processing unit
Many architects believe that major improvements in cost-energy-performance must now
come from domain-specific hardware. This paper evaluates a custom ASIC---called a Tensor …
come from domain-specific hardware. This paper evaluates a custom ASIC---called a Tensor …
PUMA: A programmable ultra-efficient memristor-based accelerator for machine learning inference
Memristor crossbars are circuits capable of performing analog matrix-vector multiplications,
overcoming the fundamental energy efficiency limitations of digital logic. They have been …
overcoming the fundamental energy efficiency limitations of digital logic. They have been …
Efficient processing of deep neural networks: A tutorial and survey
Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI)
applications including computer vision, speech recognition, and robotics. While DNNs …
applications including computer vision, speech recognition, and robotics. While DNNs …