Merge branch '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

@@ -1,3 +1,9 @@
+@article{gwennap_certus-nx_nodate,
+  author        = {Gwennap, Linley},
+  language      = {en},
+  title         = {Certus-{NX} Innovates General-Purpose {FPGAs}}
+}
+
 @inproceedings{adolf2016fathom,
  author = {Adolf, Robert and Rama, Saketh and Reagen, Brandon and Wei, Gu-yeon and Brooks, David},
  booktitle = {2016 IEEE International Symposium on Workload Characterization (IISWC)},
@@ -1251,3 +1257,21 @@ @inproceedings{zhu2018benchmarking
  url = {https://doi.org/10.1109/iiswc.2018.8573476},
  year = {2018}
 }
+
+@inproceedings{zhangfast,
+author = {Zhang, Dan and Huda, Safeen and Songhori, Ebrahim and Prabhu, Kartik and Le, Quoc and Goldie, Anna and Mirhoseini, Azalia},
+title = {A Full-Stack Search Technique for Domain Optimized Deep Learning Accelerators},
+year = {2022},
+isbn = {9781450392051},
+publisher = {Association for Computing Machinery},
+address = {New York, NY, USA},
+url = {https://doi.org/10.1145/3503222.3507767},
+doi = {10.1145/3503222.3507767},
+abstract = {The rapidly-changing deep learning landscape presents a unique opportunity for building inference accelerators optimized for specific datacenter-scale workloads. We propose Full-stack Accelerator Search Technique (FAST), a hardware accelerator search framework that defines a broad optimization environment covering key design decisions within the hardware-software stack, including hardware datapath, software scheduling, and compiler passes such as operation fusion and tensor padding. In this paper, we analyze bottlenecks in state-of-the-art vision and natural language processing (NLP) models, including EfficientNet and BERT, and use FAST to design accelerators capable of addressing these bottlenecks. FAST-generated accelerators optimized for single workloads improve Perf/TDP by 3.7\texttimes{} on average across all benchmarks compared to TPU-v3. A FAST-generated accelerator optimized for serving a suite of workloads improves Perf/TDP by 2.4\texttimes{} on average compared to TPU-v3. Our return on investment analysis shows that FAST-generated accelerators can potentially be practical for moderate-sized datacenter deployments.},
+booktitle = {Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems},
+pages = {27–42},
+numpages = {16},
+keywords = {design space exploration, hardware-software codesign, tensor processing unit, machine learning, operation fusion},
+location = {Lausanne, Switzerland},
+series = {ASPLOS '22}
+}