References

The 5 Algorithms for Efficient Deep Learning Inference on Small Devices | by James Le | Heartbeat (fritz.ai)
https://www.sciencedirect.com/science/article/pii/S2095809919306356
https://www.analyticsinsight.net/embedded-ai-and-machine-learning-adding-new-advancements-in-tech-space/
https://reader.elsevier.com/reader/sd/pii/S2351978920321922?token=94518A6EFD7B40644E99D21DEF73B4EA6E3901FB2F47C28CC46430E5887F00E26444C69152B665B2C17A971975182C49cadlab.cs.ucla.edu/~jaywang/papers/fpga16-cnn.pdf
https://www.youtube.com/watch?v=eZdOkDtYMoohttps://arxiv.org/pdf/1803.08995.pdf
https://www.semanticscholar.org/paper/Iterative-Low-Rank-Approximation-for-CNN-Kholiavchenko/5d05f66896eb237671dc9bb71fb3c34c708013c9
https://heartbeat.fritz.ai/research-guide-model-distillation-techniques-for-deep-learning-4a100801c0eb
https://arxiv.org/pdf/1503.02531.pdf
https://arxiv.org/pdf/1910.10699.pdf
https://arxiv.org/pdf/1902.03393.pdf
https://arxiv.org/pdf/1910.01348.pdf
https://arxiv.org/pdf/1910.01108.pdf
https://arxiv.org/pdf/1607.04381.pdf
https://heartbeat.fritz.ai/the-2-types-of-hardware-architectures-for-efficient-training-and-inference-of-deep-neural-networks-a034850e26dd
https://arxiv.org/pdf/1901.02067.pdf
https://siliconangle.com/2019/05/26/no-cloud-required-ais-future-edge/
https://www.theregister.com/2017/02/21/processor_tla_cpu_gpu_tpu_fpga_asic_and_now_ipu/?utm_content=buffer143eb&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer

https://www.iotforall.com/fog-vs-edge-computing-do-differences-matter
https://www.microcontrollertips.com/risc-v-artificial-intelligence-machine-learning-embedded-systems-faq/
https://www.newelectronics.co.uk/electronics-technology/ai-options-for-embedded-systems/209022/
https://www.bizety.com/2019/10/28/ai-chips-gpu-tpu-and-npu/
https://reader.elsevier.com/reader/sd/pii/S2095809919306356?token=5E12B6AC3F368FE7D1852921A2614694D35F4EA835567A374793A0153FC706B04495C5C3CF85EF2295480DF8EB88234C
https://www.sciencedirect.com/science/article/pii/S2095809919306356#b0095
https://research.aimultiple.com/ai-chip/https://analyticsindiamag.com/what-is-neuromorphic-computing-how-is-it-transforming-ai-research/
https://techxplore.com/news/2020-05-ibm-ai-benefits-phase-change-memory.html
https://www.embedded-computing.com/i/1034822-industrial-ai-machine-learning-with-2018-resource-guide/17?
https://www.ims.fraunhofer.de/en/Business_Units_and_Core_Competencies/Electronic-Assistance-Systems/Technologies/Artificial-Intelligence-for-Embedded-Systems-AIfES.html
https://towardsdatascience.com/what-is-deep-learning-33a32858ea6d
https://github.com/trekhleb/homemade-machine-learninghttps://www.programmersought.com/article/1348107931/
An Updated Survey of Efficient Hardware Architectures for Accelerating Deep Convolutional Neural Networks
https://www.sciencedaily.com/releases/2021/01/210111112151.htmhttps://towardsdatascience.com/an-introduction-to-tinyml-4617f314aa79
https://www.computerweekly.com/news/252475371/Stanford-University-finds-that-AI-is-outpacing-Moores-Law