2025. 10. 29. Wednesday 📌 TODOLIST - quantization 적용 더 알아보기 (float -> int) - dpu 2nd tasklet error debugging - embedding 차원 조절하기 (tasklet >= 16)

2025. 10. 11. SaturdayUPMEM Official Example Review

2025. 10. 10. Friday다음주 랩미팅 준비: Code Review · 구현 📌 참고자료 - UPMEM SDK: https://sdk.upmem.com/stable/030_DPURuntimeService_Tasklets.html- UPMEM Naive-GEMM: https://github.com/hhessammheidary/UPMEM-GEMM 📌 TODOLIST- UPMEM Checksum example Code Review- PIM Embedding Lookup(Python Wrapper) - MHA Implementation

2025. 09. 30. 화요일

UPMEM Hello World! Examplehttps://sdk.upmem.com/stable/02_HelloWorld.html Hello World! Example — UPMEM DPU SDK 2025.1.0 Documentation© Copyright 2015-2024, UPMEM SAS - All rights reserved.sdk.upmem.com 0. UPMEM SDK 설치하기 https://sdk.upmem.com UPMEM DPU SDKUPMEM SDK The Software Development Kit for programming and using the DPU provided by the UPMEM Acceleration platform.sdk.upmem.com tar -..

PAPI: Exploiting Dynamic Parallelism in Large Language Model Decoding with a Processing-In-Memory-Enabled Computing Systemhttps://arxiv.org/abs/2502.15470 PAPI: Exploiting Dynamic Parallelism in Large Language Model Decoding with a Processing-In-Memory-Enabled Computing SystemLarge language models (LLMs) are widely used for natural language understanding and text generation. An LLM model relies ..

Pimba: A Processing-in-Memory Acceleration for Post-Transformer Large Language Model Servinghttps://github.com/casys-kaist/pimba GitHub - casys-kaist/pimba: Official code repository for "Pimba: A Processing-in-Memory Acceleration for Post-Transformer LargeOfficial code repository for "Pimba: A Processing-in-Memory Acceleration for Post-Transformer Large Language Model Serving [MICRO'25]" - casys..

Accelerating LLMs using an Efficient GEMM Library and Target-Aware Optimizations on Real-World PIM Devices * TVM = deep learning compiler frameworkApache TVM is a machine learning compilation framework, following the principle of Python-first development and universal deployment. It takes in pre-trained machine learning models, compiles and generates deployable modules that can be embedded and..

2025. 05. 20. 화요일 Paper: https://open.library.ubc.ca/soa/cIRcle/collections/ubctheses/24/items/1.0435518 Offloading embedding lookups to processing-in-memory for deep learning recommender modelsRecommender systems are an essential part of many industries and businesses. Generating accurate recommendations is critical for user engagement and business revenue. Currently, deep learning recomme..

2025. 05. 13. PIM · TFLite 랩미팅 👩‍💻 📌 다음 시간까지 어떻게 나뉘어서 올라가는지 알고리즘 구현 조사하기https://open.library.ubc.ca/soa/cIRcle/collections/ubctheses/24/items/1.0435518 Offloading embedding lookups to processing-in-memory for deep learning recommender modelsAbstract Recommender systems are an essential part of many industries and businesses. Generating accurate recommendations is critical for user engagement..