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Faster R-CNN https://arxiv.org/pdf/1506.01497.pdf
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ImageNet https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5206848
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Attention Is All You Need https://arxiv.org/pdf/1706.03762.pdf
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Swing Transformer https://arxiv.org/abs/2103.14030
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Vision Transformer https://arxiv.org/pdf/2010.11929.pdf
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Stable Diffusion https://arxiv.org/pdf/2112.10752.pdf
1)- Why Systolic Architecture ?
http://www.eecs.harvard.edu/~htk/publication/1982-kung-why-systolic-architecture.pdf
2)- Anatomy of High Performance Matrix Multiccation
https://www.cs.utexas.edu/users/pingali/CS378/2008sp/papers/gotoPaper.pdf
3)- Efficient Processing of Deep Neural Networks: A Tutorial and Survey
https://www.rle.mit.edu/eems/wp-content/uploads/2017/11/2017_pieee_dnn.pdf
4)- In-Datacenter Performance Analysis of a Tensor Processing Unit
https://arxiv.org/pdf/1704.04760.pdf
5)- SqueezeNext: Hardware-Aware Neural Network Design
6)- A Systematic Approach toBlocking Convolutional Neural Networks
https://arxiv.org/pdf/1606.04209.pdf
7)- High Performance Zero-Memory Overhead Direct Convolutions
https://arxiv.org/pdf/1809.10170.pdf
8)- MIXED PRECISION TRAINING
https://arxiv.org/pdf/1710.03740.pdf
9)- Mixed Precision Training With 8-bit Floating Point
https://arxiv.org/pdf/1905.12334.pdf
10)- A taxonomy of acceleratorarchitectures and their programming models
https://pdfs.semanticscholar.org/dee4/9d30a19f392ca9d002720a554800fa16d19e.pdf
11)- cuDNN: Efficient Primitives for Deep Learning
https://arxiv.org/pdf/1410.0759.pdf
12)- Eyeriss: A Spatial Architecture for Energy-Efficient Dataflowfor Convolutional Neural Networks
https://www.rle.mit.edu/eems/wp-content/uploads/2016/04/eyeriss_isca_2016.pdf
13)- Deep Learning Inference in Facebook Data Centers: Characterization,Performance Optimizations and Hardware Implications
https://arxiv.org/pdf/1811.09886.pdf
14)- In-Memory Computing: Advances and Prospects
https://ieeexplore.ieee.org/document/8811809
15)- Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
https://arxiv.org/pdf/1510.00149.pdf
16)- Binarized Neural Networks
https://papers.nips.cc/paper/2016/file/d8330f857a17c53d217014ee776bfd50-Paper.pdf
17)- XNOR-Net: ImageNet Classification Using BinaryConvolutional Neural Networks
https://arxiv.org/pdf/1603.05279.pdf
18)- NetAdapt: Platform-Aware Neural NetworkAdaptation for Mobile Applications
19)- Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks
https://dl.acm.org/doi/10.1145/2684746.2689060
20)- Memory Requirements for Convolutional Neural Network Hardware Accelerators
https://ieeexplore.ieee.org/abstract/document/8573527
21)-DianNao: a small-footprint high-throughput accelerator for ubiquitous machine-learning
https://dl.acm.org/doi/10.1145/2654822.2541967
22)- TETRIS: Scalable and Efficient Neural Network Acceleration with 3D Memory
https://dl.acm.org/doi/10.1145/3093337.3037702
23)- Neural Cache: Bit-Serial In-Cache Acceleration of Deep Neural Networks
http://blaauw.engin.umich.edu/wp-content/uploads/sites/342/2018/10/Eckert-Naural-Cache.pdf
24)- Cambricon-X: An accelerator for sparse neural networks
https://ieeexplore.ieee.org/document/7783723
25)- UCNN: Exploiting Computational Reuse in DeepNeural Networks via Weight Repetition
https://arxiv.org/pdf/1804.06508.pdf
26)- Hardware for Machine Learning:Challenges and Opportunities
https://arxiv.org/pdf/1612.07625.pdf
27)- Bit Fusion: Bit-Level Dynamically Composable rchitecture for Accelerating Deep Neural Networks
https://arxiv.org/pdf/1712.01507.pdf
28)- SCNN: An Accelerator for Compressed-sparseConvolutional Neural Networks
https://people.csail.mit.edu/anurag_m/papers/2017.scnn.isca.pdf
29)- EIE: Efficient Inference Engine on Compressed Deep Neural Network
https://www.cs.virginia.edu/~smk9u/CS6501F16/p243-han.pdf
30)- Understanding Sources of Inefficiency in General-Purpose Chips
http://csl.stanford.edu/~christos/publications/2010.efficiency.isca.pdf
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