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유학생

대한민국 서울
Joined November 2017
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Replying to @Jzurde
Mallory fork steak :)
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논문 PDF만 쌓이고, 읽고 나서도 남는 게 없다면 이 3개 저장해두면 됨. 관련 논문 찾기 → 막힌 내용 질문하기 → 근거와 메모 남기기. 관심 분야를 처음 공부하거나 연구 주제를 탐색할 때 쓰는 조합임. 1. ResearchRabbit 논문 하나를 기준으로 비슷한 연구와 앞뒤로 이어지는 연구를 찾아보기. “이 논문 다음에는 뭘 읽어야 하지?” 싶을 때 쓰기 좋음. 기본 검색과 논문 모음 기능은 무료임. researchrabbit.ai/ 2. Gemini Notebook — 기존 NotebookLM 논문 PDF를 넣고 연구 문제·방법·결과를 질문하기. 이해가 안 되는 부분을 좁혀 묻고, 답변에 붙은 출처로 원문을 확인할 때 쓰면 됨. notebook.google/ 3. Zotero 논문과 메모를 함께 보관하고, 밑줄 친 부분을 출처가 연결된 노트로 모으기. 나중에 “이 설명이 어느 논문에 있었지?”를 다시 찾을 때 쓰기 좋음. zotero.org/ 논문 찾기 → 질문하며 읽기 → 원문 근거와 메모 저장하기. 처음에는 논문 한 편에서 ‘무슨 문제를 풀었는지, 어떻게 풀었는지, 어디까지 확인했는지’만 남겨보면 됨. AI 요약을 모으는 것보다, 내가 설명할 수 있는 내용과 다시 확인할 근거를 함께 남기는 데 쓰면 좋음.
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Catscan: Visualizing Pipelines of CPU Performance Simulation Aaron Lindsay, Nicholas Kelly, Scott Witscher, Mahesh Madhav arxiv.org/abs/2610.02121 [𝚌𝚜.𝙰𝚁 𝚌𝚜.𝙷𝙲 𝚌𝚜.𝙿𝙵]
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댕댕 retweeted
reading about zipserv today arxiv.org/pdf/2603.17435
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Replying to @issaimaru
I'm always rooting for you :)
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Replying to @issaimaru
Your implementation and research results are truely impressive!! Amazing work!
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The KV Cache Is the New Memory Wall Tejinder Singh arxiv.org/abs/2609.30854 [𝚌𝚜.𝙳𝙲 𝚌𝚜.𝙻𝙶 𝚌𝚜.𝙿𝙵]
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수강신청할때 지도교수님이랑 상의를 해야하나...
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Optimizing AI Inference Across the Deployment Stack 🔗 arxiv.org/abs/2609.10550
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【RISC-V Day Tokyo 2026 Autumn】参加登録開始! 📅 11月11日(水) @東京大学本郷キャンパス 午前はチップを設計、午後は実物を見る一日。 チケット: ・一般参加(無償) ・半導体回路設計・試作コース ¥3,000 ・書籍付き ¥1,500 ▶[peatix.com/event/5190002/das…] #RISCV #RISCVDayTokyo #半導体
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댕댕 retweeted
传统大模型为了算得快,把几个字母拼成一个 Token,搞了个 32K~128K 的超大词表。 但到了 1B 这种小模型上,灾难来了: 底噪丢失:大模型教小模型(蒸馏)时,词表太大只能截取 Top-K 概率,长尾的细微“思考逻辑”全被丢弃了。 显存黑洞:光是存那张巨型词表和概率矩阵,小设备的显存就先被吃掉一大半。 这就像给小学生灌输一套只有博士才懂的晦涩缩写,小模型自然学不动。
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I put together a handbook on how Mixture of Experts actually works, starting with what happens when a Transformer FFN becomes a routed bank of experts and only a small subset is selected for each token. It works through router logits, Top-k selection, expert weighting, load balancing, capacity and token dropping, router z-loss, dropless execution, total vs. active parameters, expert parallelism, and how these ideas show up in Mixtral, DeepSeek-V3, and Qwen3. Worked examples throughout, with citations back to the original papers and current implementations. Made it to be the explanation I wish I'd had when I first tried to understand how modern MoE models can grow so large without activating the full expert bank for every token.
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FlashGEMM: Optimizing Sequences of Matrix Multiplication by Exploiting Data Reuse on CPUs 🔗 dl.acm.org/doi/full/10.1145/…
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Inference roadmap I gotta follow: 1) Watch this course fully : youtube.com/playlist?list=PL… (Covers everything related to LLM Inference+ Training but on high level) 2) Gonna read this resource: jax-ml.github.io/scaling-boo… ( for deep dive on topics) 3) Read PMPP(CUDA & GPU specific chapters) 4) Blogs and codebase of vLLM , SGlang, llama.cpp 5) Inference Engg by philip book for brush up on the concepts 6) Also these notes which i found randomly on X for brushup: nitter.gq/gauri__gupta/status/20… Along with it : contributions to the above mentioned repo for understanding depth + talking with folks building in inference here
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inference is future. tried going as much in detail as possible for the writing GEMV kernel in CUDA, hopefully it will be a good read. ciao! heyyanshuman.com/posts/makin…
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Excited to co-instruct MIT 6.5940: TinyML and Efficient AI Computing with @songhan_mit this fall! Looking forward to teaching, learning, and building with an incredible class. Course site: efficientml.ai
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9/10テスト販売『つくりながら学ぶ! #リアルタイムOS自作入門』#マイナビ出版 (9784839991876)#矢野倉伊織 著 ◆「#自作OS」「#Arduino」棚にて展開中!#ArduinoUnoR4WiFi 上でリアルタイムOS(オペレーティングシステム)を一から自作しながら学べます!
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댕댕 retweeted
i trained a computer vision model to train me at tennis it tracks ball speed, forehand vs backhand strokes, shot placement, body position when i strike the ball how this works: - input video from an iphone - CV pipeline orchestrated with roboflow agent: data labelling using Astra 6, detection done with a fine-tuned RF-DETR model - video annotation using python + openCV goal is to make an app that records each practice session, tracks progress over time, and gives feedback on improving my technique more to come!
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