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Efficient acceleration of deep learning inference on resource-constrained edge devices: A review
Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …
in breakthroughs in many areas. However, deploying these highly accurate models for data …
Llmcad: Fast and scalable on-device large language model inference
Compiler Technologies in Deep Learning Co-Design: A Survey
With the rapid development of deep learning applications, general-purpose processors no
longer suffice for deep learning workloads because of the dying of Moore's Law. Thus …
longer suffice for deep learning workloads because of the dying of Moore's Law. Thus …
EdgeLLM: Fast On-device LLM Inference with Speculative Decoding
Generative tasks, such as text generation and question answering, are essential for mobile
applications. Given their inherent privacy sensitivity, executing them on devices is …
applications. Given their inherent privacy sensitivity, executing them on devices is …
Cloud and Edge Computing for Connected and Automated Vehicles
The recent development of cloud computing and edge computing shows great promise for
the Connected and Automated Vehicle (CAV), by enabling CAVs to offload their massive on …
the Connected and Automated Vehicle (CAV), by enabling CAVs to offload their massive on …
Rise of the autonomous machines
We are entering the age of autonomous machines, but many roadblocks exist on the path to
make this a reality. We make a preliminary attempt at recognizing and categorizing the …
make this a reality. We make a preliminary attempt at recognizing and categorizing the …