Preamble for PIM chapter

src/doc.bib

@@ -394,6 +394,23 @@
   file = {/home/derek/Nextcloud/Verschiedenes/Zotero/storage/3XHCI9KG/Oliveira et al. - 2023 - DaPPA A Data-Parallel Framework for Processing-in.pdf}
 }
 
+@techreport{radojkovic2021,
+  title = {Processing in {{Memory}}: {{The Tipping Point}}},
+  shorttitle = {Processing in {{Memory}}},
+  author = {Radojkovi{\'c}, Petar and Carpenter, Paul and {Esmaili-Dokht}, Pouya and Cimadomo, R{\'e}my and Charles, Henri-Pierre and Sebastian, Abu and Amato, Paolo},
+  year = {2021},
+  month = jul,
+  institution = {{Zenodo}},
+  doi = {10.5281/ZENODO.4767489},
+  url = {https://zenodo.org/record/4767489},
+  urldate = {2024-02-06},
+  abstract = {Decades after being initially explored in the 1970s, Processing in Memory (PIM) is currently experiencing a renaissance. By moving part of the computation to the memory devices, PIM addresses a fundamental issue in the design of modern computing systems, the mismatch between the von Neumann architecture and the requirements of important data-centric applications. A number of industrial prototypes and products are under development or already available in the marketplace, and these devices show the potential for cost-effective and energy-efficient acceleration of HPC, AI and data analytics workloads. This paper reviews the reasons for the renewed interest in PIM and surveys industrial prototypes and products, discussing their technological readiness. Wide adoption of PIM in production, however, depends on our ability to create an ecosystem to drive and coordinate innovations and co-design across the whole stack. European companies and research centres should be involved in all aspects, from technology, hardware, system software and programming environment, to updating of the algorithm and application. In this paper, we identify the main challenges that must be addressed and we provide guidelines to prioritise the research efforts and funding. We aim to help make PIM a reality in production HPC, AI and data analytics.},
+  copyright = {Creative Commons Attribution 4.0 International, Open Access},
+  langid = {english},
+  keywords = {not read,PIM,Processing in Memory},
+  file = {/home/derek/Nextcloud/Verschiedenes/Zotero/storage/U92WPM5C/Radojković et al. - 2021 - Processing in Memory The Tipping Point.pdf}
+}
+
 @inproceedings{seshadri2013,
   title = {{{RowClone}}: Fast and Energy-Efficient in-{{DRAM}} Bulk Data Copy and Initialization},
   shorttitle = {{{RowClone}}},
@@ -511,3 +528,21 @@
   archiveprefix = {arxiv},
   file = {/home/derek/Nextcloud/Verschiedenes/Zotero/storage/MGQYNDPQ/Touvron et al. - 2023 - LLaMA Open and Efficient Foundation Language Mode.pdf;/home/derek/Nextcloud/Verschiedenes/Zotero/storage/YDAT8K7L/2302.html}
 }
+
+@article{zou2021,
+  title = {Breaking the von {{Neumann}} Bottleneck: Architecture-Level Processing-in-Memory Technology},
+  shorttitle = {Breaking the von {{Neumann}} Bottleneck},
+  author = {Zou, Xingqi and Xu, Sheng and Chen, Xiaoming and Yan, Liang and Han, Yinhe},
+  year = {2021},
+  month = jun,
+  journal = {Science China Information Sciences},
+  volume = {64},
+  number = {6},
+  pages = {160404},
+  issn = {1674-733X, 1869-1919},
+  doi = {10.1007/s11432-020-3227-1},
+  url = {https://link.springer.com/10.1007/s11432-020-3227-1},
+  urldate = {2024-02-06},
+  langid = {english},
+  file = {/home/derek/Nextcloud/Verschiedenes/Zotero/storage/7BKACKF8/Zou et al. - 2021 - Breaking the von Neumann bottleneck architecture-.pdf}
+}