Weekly Schedule
| Week 1 1/12-1/16 | Lecture Topic | Assignment |
|---|---|---|
| Mon | Course Introduction | Welcome B. Dally et. al Domain-Specific Hardware Accelerators Comm of the ACM 2020 |
| Weds | Technology Trends Review | Review from Chapter 1 Computer Architecture: A Quantitative Approach, Hennessy and Patterson |
| Fri | Moore’s Law, Dennard Scaling Review | Reading: Cramming more components onto integrated circuits |
| Week 2 1/19-1/23 | Lecture Topic | Assignment |
| Mon | Martin Luther King Day | |
| Weds | Roofline Model | Roofline: an insightful visual performance model for multicore architectures](https://dl.acm.org/doi/10.1145/1498765.1498785) S. Williams et. al. |
| Fri | Performance Measuring Recap | |
| Week 3 1/26-1/30 | Lecture Topic | Assignment |
| Mon | General Purpose Processors and the Virtuous Cycle | Reading: N. Thompson, S. Spanuth The decline of computers as a general purpose technology A. Fuchs, D. Wentzlaff The Accelerator Wall: Limits of Chip Specialization |
| Weds | General Purpose Processors and the Virtuous Cycle | Reading: N. Thompson, S. Spanuth The decline of computers as a general purpose technology A. Fuchs, D. Wentzlaff |
| Fri | Machine Learning Boot Camp | Reading: H&P Comp Arch: a Quant. Approach Ch 7.3-4 Optional: Implications of Makimoto’s Wave T. Makimoto |
| Week 4 2/2-2/6 | Lecture Topic | Assignment |
| Mon | Machine Learning Boot Camp MLPs | Reading: H&P Comp Arch: a Quant. Approach Ch 7.3-4 Optional: Implications of Makimoto’s Wave T. Makimoto |
| Weds | Machine Learning Boot Camp - Systolic Arrays | Reading: H&P Comp Arch: a Quant. Approach Ch 7.3-4 Optional: Implications of Makimoto’s Wave T. Makimoto |
| Fri | Machine Learning Boot Camp CNNs | Reading: H&P Comp Arch: a Quant. Approach Ch 7.3-4 Optional: Implications of Makimoto’s Wave T. Makimoto |
| Week 5 2/9-2/13 | Lecture Topic | Assignment |
| Mon | Computational Power and AI | Read Computational Power and AI |
| Weds | Computational Power and AI | Read Computational Power and AI |
| Fri | In-Datacenter Performance Analysis of a Tensor Processing Unit | In-Datacenter Performance Analysis of a Tensor Processing Unit N. Jouppi et. al. |
| Week 6 2/16-2/20 | Lecture Topic | Assignment |
| Mon | In-Datacenter Performance Analysis of a Tensor Processing Unit | In-Datacenter Performance Analysis of a Tensor Processing Unit N. Jouppi et. al. |
| Mon | In-Datacenter Performance Analysis of a Tensor Processing Unit | In-Datacenter Performance Analysis of a Tensor Processing Unit N. Jouppi et. al. |
| Fri | Processor In Memory Architectures | |
| Week 7 2/23-2/27 | Lecture Topic | Assignment |
| Mon | Processor In Memory Architectures | |
| Weds | AUP-ZU3 | AMD Vivado Install AUP-Zu3 ref manual |
| Fri | Paper Selections | |
| Week 8 3/2-3/6 | Lecture Topic | Assignment |
| Mon | Presentation Assignments | |
| Weds | How to give a bad presentation | |
| Fri | Emerging NVMs | |
| Week 9 3/9-3/13 | Lecture Topic | Assignment |
| Mon | ||
| Weds | ||
| Fri | ||
| Week 10 3/16-3/20 | Lecture Topic | Assignment |
| Mon | Group #1: Randall,Pretha | Cerebras Architecture Deep Dive: First Look Inside the Hardware/Software Co-Design for Deep Learning IEEE Micro Volume: 43, Issue: 3, May-June 2023 |
| Weds | Group #2: Parker, Braylon | N. Jouppi et. al. Ten Lessons From Three Generations Shaped Google’s TPUv4i Proceedings of the ACM/IEEE 48th Annual International Symposium on Computer Architecture (ISCA), 2021 |
| Fri | Group #3 Hunter, Chance | Jack Kendall, Suhas Kumar The building blocks of a brain-inspired computer Applied Physics Reviews, Volume 7, Issue 1, March 2020 |
| Week 11 3/23-3/27 | Lecture Topic | Assignment |
| Mon | Spring Break Yah ! | |
| Weds | Spring Break Yah ! | |
| Fri | Spring Break Yah ! | |
| Week 12 3/30-4/3 | Lecture Topic | Assignment |
| Mon | Laboratory discussion | Bring in your boards |
| Weds | Group #4: Ryan, Joseph, Phillip | The true Processor in Memory AcceleartorF. Devaux, IEEE Hot Chips 31 Symposium (HCS) 2019 |
| Fri | Group #5: Chase, Jacob | Y. Chen and M. S. Abdelfattah, “BRAMAC: Compute-in-BRAM Architectures for Multiply-Accumulate on FPGAs,” 2023 IEEE 31st Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), Marina Del Rey, CA, USA, 2023, pp. 52-62, doi: 10.1109/FCCM57271.2023.00015 |
| Week 13 4/6-4/10 | Lecture Topic | Assignment |
| Mon | Group #6:Christian, Max | Dennis Abts et. al. Think Fast: A Tensor Streaming Processor (TSP) for Accelerating Deep Learning Workloads Proceedings of the ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA), 2020 |
| Weds | Group #7: McKyala,Nathan,Christian | K. Asi Fuzzaman et. al. A Survey on processing-in-memory techniques: Advances and challenges |
| Fri | Final Project Discussion | |
| Week 14 4/13-4/17 | Lecture Topic | Assignment |
| Mon | Individual Group Preparation | |
| Weds | Group #1 Ryan, Pretha, Phillip | J. Gomez et. al.,Benchmarking Memory-Centric Computing Systems: Analysis of Real Processing-In-Memory Hardware Proceedings of the 12th International Green and Sustainable Computing Conference (IGSC), 2021 |
| Fri | Group #2 Chase, Jacob | A. Arora et. al., CoMeFa Compute in Memory Blocks for FPGAs Proceedings of the 30th IEEE International Symposium on Field-Programmable Custom Computing Machines, May 15-18, 2022 |
| Week 15 4/20-4/24 | Lecture Topic | Assignment |
| Mon | Group #3 Randall, Joseph | A. Rico etl.al, AMD XDNA NPU in Ryzen AI Processors IEEE Micro Nov-Dec 2024 |
| Weds | Group #4 MyKala, Nathan, Christian | S. Lee et. al. Hardware Architecture and Software Stack for PIM Based on Commercial DRAM Technology Proceedings of the ACM/IEEE 48th Annual International Symposium on Computer Architecture (ISCA), 2021. |
| Fri | Group #5 Christian, Max Dennis | Abts et. al. A Software-defined Tensor Streaming Multiprocessor for Large-scale Machine Learning Proceedings of the ACM/IEEE 48th Annual International Symposium on Computer Architecture (ISCA), 2021. |
| Week 16 4/27-5/1 | Lecture Topic | Assignment |
| Mon | Group #6 Hunter, Chance | Accelerating Neural Network Inference with Processing-in-DRAM:From the Edge to the CloudG. Oliveira et. al.,extended and updated version of a paper published in IEEE Micro, pp. 1-14, 29 Aug. 2022. |
| Weds | Group #7 Parker, Braylon | In Memory Intelligence Tim Finkbeiner et. al., IEEE Micro Volume: 37, Issue: 4, 2017 |
| Fri | Reading Day | All Done! |
| Final | Monday May 4th 10:15am - 12:15pm |
S. Lee et. al., Hardware Architecture and Software Stack for PIM Based on Commercial DRAM Technology Proceedings of the ACM/IEEE 48th Annual International Symposium on Computer Architecture (ISCA), 2021 G. Kim et. al.,SK Hynix AI-Specific Computing Memory Solution: From AiM Device to Heterogeneous AiMX-xPU System for Comprehensive LLM Inference
A Gajjar et. al.,Azure-Lily: An FPGA Architecture with Analog IMC Engines for Efficient AI ACM Transactions on Architecture and Code Optimization, Volume 23, Issue 1 Article No.: 29, Pages 1 - 26
X. Wang et. al.,Compute Capable Block RAMs for Efficient Deep Learning Acceleration on FPGAs Proceedings of the 29th IEEE International Symposium on Field-Programmable Custom Computing Machines, 2021
RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing Proceedings of the 47th ACM/IEEE International Symposium on Computer Architecture (ISCA) Valencia, Spain, 2020, pp. 790-803, doi: 10.1109/ISCA45697.2020.00070.
Memory-Centric Computing with SK hynix’s Domain-Specific Memory 2023 Y. Kwon et. al.,IEEE Hot Chips 35 Symposium (HCS) 2023
J.D. Kendall, S. Kumar The building blocks of a brain-inspired computer Appl. Phys. Rev. 1 March 2020; 7 (1): 011305.
Tutorial on Memory-Centric Computing
Accelerating Neural Network Inference with Processing-in-DRAM:From the Edge to the CloudG. Oliveira et. al.,extended and updated version of a paper published in IEEE Micro, pp. 1-14, 29 Aug. 2022.
Jin Hyun Kim Aquabolt-XL: Samsung HBM2-PIM with in-memory processing for ML accelerators and beyond IEEE Hot Chips 33, 2021