81 figure references/part 2

contents/ai_for_good/ai_for_good.qmd

@@ -28,13 +28,13 @@ By aligning AI progress with human values, goals, and ethics, the ultimate goal
 
 ## Introduction
 
-To give ourselves a framework around which to think about AI for social good, we will be following the UN Sustainable Development Goals (SDGs). The UN SDGs are a collection of 17 global goals adopted by the United Nations in 2015 as part of the 2030 Agenda for Sustainable Development. The SDGs address global challenges related to poverty, inequality, climate change, environmental degradation, prosperity, and peace and justice.
+To give ourselves a framework around which to think about AI for social good, we will be following the UN Sustainable Development Goals (SDGs). The UN SDGs are a collection of 17 global goals, shown in @fig-sdg, adopted by the United Nations in 2015 as part of the 2030 Agenda for Sustainable Development. The SDGs address global challenges related to poverty, inequality, climate change, environmental degradation, prosperity, and peace and justice.
 
 What is special about SDGs is that they are a collection of interlinked objectives designed to serve as a "shared blueprint for peace and prosperity for people and the planet, now and into the future.". The SDGs emphasize the interconnected environmental, social and economic aspects of sustainable development by putting sustainability at their center.
 
 A recent study [@vinuesa2020role] highlights the influence of AI on all aspects of sustainable development, in particular on the 17 Sustainable Development Goals (SDGs) and 169 targets internationally defined in the 2030 Agenda for Sustainable Development. The study shows that AI can act as an enabler for 134 targets through technological improvements, but it also highlights the challenges of AI on some targets. When considering AI and societal outcomes, the study shows that AI can benefit 67 targets, but it also warns about the issues related to the implementation of AI in countries with different cultural values and wealth.
 
-[![United Nations Sustainable Development Goals (SDG)](https://www.un.org/sustainabledevelopment/wp-content/uploads/2015/12/english_SDG_17goals_poster_all_languages_with_UN_emblem_1.png)](https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.un.org%2Fsustainabledevelopment%2Fblog%2F2015%2F12%2Fsustainable-development-goals-kick-off-with-start-of-new-year%2F&psig=AOvVaw1vppNt_HtUx3YM8Tzd7s_-&ust=1695950945167000&source=images&cd=vfe&opi=89978449&ved=0CBAQjRxqFwoTCOCG1t-TzIEDFQAAAAAdAAAAABAD)
+[![United Nations Sustainable Development Goals (SDG). Credit: United Nations.](https://www.un.org/sustainabledevelopment/wp-content/uploads/2015/12/english_SDG_17goals_poster_all_languages_with_UN_emblem_1.png)](https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.un.org%2Fsustainabledevelopment%2Fblog%2F2015%2F12%2Fsustainable-development-goals-kick-off-with-start-of-new-year%2F&psig=AOvVaw1vppNt_HtUx3YM8Tzd7s_-&ust=1695950945167000&source=images&cd=vfe&opi=89978449&ved=0CBAQjRxqFwoTCOCG1t-TzIEDFQAAAAAdAAAAABAD){#fig-sdg}
 
 In the context of our book, here is how TinyML could potentially help advance at least _some_ of these SDG goals.  
 

contents/hw_acceleration/hw_acceleration.bib

@@ -1,7 +1,8 @@
-@article{gwennap_certus-nx_nodate,
+@techreport{gwennap_certus-nx_nodate,
   author        = {Gwennap, Linley},
-  language      = {en},
-  title         = {Certus-{NX} Innovates General-Purpose {FPGAs}}
+  title         = {White paper: Certus-NX Innovates General-Purpose FPGAs.},
+  year          = {2020},
+  institution   = {The Linely Group, sponsored by Lattice}
 }
 
 @article{mattson2020mlperf,
@@ -1250,4 +1251,11 @@ @inproceedings{zhangfast
 keywords = {design space exploration, hardware-software codesign, tensor processing unit, machine learning, operation fusion},
 location = {Lausanne, Switzerland},
 series = {ASPLOS '22}
+}
+
+@article{rayis2014,
+author = {El-Rayis, A.O.},
+title = {Reconfigurable architectures for the next generation of mobile device telecommunications systems},
+year = {2014},
+url = {: https://www.researchgate.net/publication/292608967}
 }

contents/ondevice_learning/ondevice_learning.bib

@@ -326,3 +326,19 @@ @inproceedings{gruslys2016memory
   url           = {https://proceedings.neurips.cc/paper/2016/hash/a501bebf79d570651ff601788ea9d16d-Abstract.html},
   year          = {2016}
 }
+
+@article{zhuang2021comprehensive,
+  title         = {A Comprehensive Survey on Transfer Learning},
+  author        = {Fuzhen Zhuang and Zhiyuan Qi and Keyu Duan and Dongbo Xi and Yongchun Zhu and Hengshu Zhu and Hui Xiong and Qing He},
+  journal       = {Proceedings of the IEEE},
+  year          = {2021},
+  url           = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9134370}
+}
+
+@inproceedings{cai2020tinytl,    
+  title         = {TinyTL: Reduce Memory, Not Parameters for Efficient On-Device Learning},    
+  author        = {Cai, Han and Gan, Chuang and Zhu, Ligeng and Han, Song},
+  booktitle     = {Advances in Neural Information Processing Systems},  
+  volume        = {33},
+  year          = {2020}
+}