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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 …
Adapting neural networks at runtime: Current trends in at-runtime optimizations for deep learning
Adaptive optimization methods for deep learning adjust the inference task to the current
circumstances at runtime to improve the resource footprint while maintaining the model's …
circumstances at runtime to improve the resource footprint while maintaining the model's …
Pruning and quantization for deep neural network acceleration: A survey
Deep neural networks have been applied in many applications exhibiting extraordinary
abilities in the field of computer vision. However, complex network architectures challenge …
abilities in the field of computer vision. However, complex network architectures challenge …
Dynamic neural networks: A survey
Dynamic neural network is an emerging research topic in deep learning. Compared to static
models which have fixed computational graphs and parameters at the inference stage …
models which have fixed computational graphs and parameters at the inference stage …
Binary neural networks: A survey
The binary neural network, largely saving the storage and computation, serves as a
promising technique for deploying deep models on resource-limited devices. However, the …
promising technique for deploying deep models on resource-limited devices. However, the …
Not all images are worth 16x16 words: Dynamic transformers for efficient image recognition
Abstract Vision Transformers (ViT) have achieved remarkable success in large-scale image
recognition. They split every 2D image into a fixed number of patches, each of which is …
recognition. They split every 2D image into a fixed number of patches, each of which is …
Forward and backward information retention for accurate binary neural networks
Weight and activation binarization is an effective approach to deep neural network
compression and can accelerate the inference by leveraging bitwise operations. Although …
compression and can accelerate the inference by leveraging bitwise operations. Although …
The lazy neuron phenomenon: On emergence of activation sparsity in transformers
This paper studies the curious phenomenon for machine learning models with Transformer
architectures that their activation maps are sparse. By activation map we refer to the …
architectures that their activation maps are sparse. By activation map we refer to the …
Sanger: A co-design framework for enabling sparse attention using reconfigurable architecture
In recent years, attention-based models have achieved impressive performance in natural
language processing and computer vision applications by effectively capturing contextual …
language processing and computer vision applications by effectively capturing contextual …
Pnp-detr: Towards efficient visual analysis with transformers
Recently, DETR pioneered the solution of vision tasks with transformers, it directly translates
the image feature map into the object detection result. Though effective, translating the full …
the image feature map into the object detection result. Though effective, translating the full …