Apple Intelligence
https://www.apple.com/jp/apple-intelligence/
1 arXiv:2507.13575
Apple Intelligence Foundation Language Models: Tech Report 2025
Authors: Hanzhi Zhou, Erik Hornberger, Pengsheng Guo, Xiyou Zhou, Saiwen Wang, Xin Wang, Yifei He, Xuankai Chang, Rene Rauch, Louis D'hauwe, John Peebles, Alec Doane, Kohen Chia, Jenna Thibodeau, Zi-Yi Dou, Yuanyang Zhang, Ruoming Pang, Reed Li, Zhifeng Chen, Jeremy Warner, Zhaoyang Xu, Sophy Lee, David Mizrahi, Ramsey Tantawi, Chris Chaney , et al. (370 additional authors not shown)
Abstract: …two multilingual, multimodal foundation language models that power Apple Intelligence features across Apple devices and services: i a 3B-parameter on-device model optimized for Apple silicon through architectural innovations such as KV-cache sharing and 2-bit quantization-aware training; and ii a scalable server model… ▽ More
Submitted 17 July, 2025; originally announced July 2025.
2 arXiv:2506.23635
doi 10.1145/3649601.3698722
Towards Building Private LLMs: Exploring Multi-Node Expert Parallelism on Apple Silicon for Mixture-of-Experts Large Language Model
Authors: Mu-Chi Chen, Po-Hsuan Huang, Xiangrui Ke, Chia-Heng Tu, Chun Jason Xue, Shih-Hao Hung
Abstract: Large Language Models (LLMs) have revolutionized Artificial Intelligence (AI) with significant advancements such as OpenAI's ChatGPT, Meta's Llama, and Databricks' DBRX. This paper addresses the cost and scalability challenges encountered when constructing private LLM systems for personal or small group services, as aimed by Apple Intelligence. A Mac Studio cluster with Apple's M2 Ultra chips is e… ▽ More
Submitted 30 June, 2025; originally announced June 2025.
Comments: International Conference on Research in Adaptive and Convergent Systems (RACS '24), November 5--8, 2024, Pompei, Italy
ACM Class: I.6.4; I.2.7; I.2.11
3 arXiv:2506.14606
Guaranteed Guess: A Language Modeling Approach for CISC-to-RISC Transpilation with Testing Guarantees
Authors: Ahmed Heakl, Sarim Hashmi, Chaimaa Abi, Celine Lee, Abdulrahman Mahmoud
Abstract: …correctness of 99% on HumanEval programs and 49% on BringupBench programs, respectively. Further, we compare our approach to the state-of-the-art Rosetta 2 framework on Apple Silicon, showcasing 1.73x faster runtime performance, 1.47x better energy efficiency, and 2.41x better memory usage for our transpiled code, demo… ▽ More
Submitted 17 June, 2025; originally announced June 2025.
Comments: Project page: https://ahmedheakl.github.io/Guaranteed-Guess/
4 arXiv:2505.04066
LLAMAPIE: Proactive In-Ear Conversation Assistants
Authors: Tuochao Chen, Nicholas Batchelder, Alisa Liu, Noah Smith, Shyamnath Gollakota
Abstract: …We evaluate our approach on real-world datasets, demonstrating its effectiveness in providing helpful, unobtrusive assistance. User studies with our assistant, implemented on Apple Silicon M2 hardware, show a strong preference for the proactive assistant over both a baseline with no assistance and a reactive model, hi…
Submitted 28 July, 2025; v1 submitted 6 May, 2025; originally announced May 2025.
Comments: Published by ACL2025 (Findings)
5 arXiv:2504.13821
Toward Portable GPU Performance: Julia Recursive Implementation of TRMM and TRSM
Authors: Vicki Carrica, Maxwell Onyango, Rabab Alomairy, Evelyne Ringoot, James Schloss, Alan Edelman
Abstract: …multiple dispatch and metaprogramming together with the GPUArrays and KernelAbstractions frameworks, we expose a single hardware-agnostic API that runs on NVIDIA, AMD, and Apple… ▽ More
Submitted 18 April, 2025; originally announced April 2025.
6 arXiv:2502.05317
Apple vs. Oranges: Evaluating the Apple Silicon M-Series SoCs for HPC Performance and Efficiency
Authors: Paul Hübner, Andong Hu, Ivy Peng, Stefano Markidis
Abstract: This paper investigates the architectural features and performance potential of the Apple…
Submitted 25 March, 2025; v1 submitted 7 February, 2025; originally announced February 2025.
7 arXiv:2502.01651
Fine-tuning LLaMA 2 interference: a comparative study of language implementations for optimal efficiency
Authors: Sazzad Hossain, Touhidul Alam Seyam, Avijit Chowdhury, Munis Xamidov, Rajib Ghose, Abhijit Pathak
Abstract: …strategies for parallel processing and hardware utilization. Furthermore, we investigate the Mojo SDK, a novel framework designed for large language model (LLM) inference on Apple…
Submitted 30 January, 2025; originally announced February 2025.
Comments: 11 pages, conference paper. International conference on Artificial Intelligence and Future Civilization
8 arXiv:2501.14925
Profiling Apple Silicon Performance for ML Training
Authors: Dahua Feng, Zhiming Xu, Rongxiang Wang, Felix Xiaozhu Lin
Abstract: Apple… ▽ More
Submitted 28 January, 2025; v1 submitted 24 January, 2025; originally announced January 2025.
9 arXiv:2403.09919
Recurrent Drafter for Fast Speculative Decoding in Large Language Models
Authors: Yunfei Cheng, Aonan Zhang, Xuanyu Zhang, Chong Wang, Yi Wang
Abstract: …in real environments, we also validated its effectiveness for on-device applications by implementing the approach in MLX and benchmarking performance on Metal GPUs in Apple Silicon chips, achieving up to 2.3x speedup. ▽ More
Submitted 13 December, 2024; v1 submitted 14 March, 2024; originally announced March 2024.
10 arXiv:2401.11455
Study on the Particle Sorting Performance for Reactor Monte Carlo Neutron Transport on Apple Unified Memory GPUs
Authors: Changyuan Liu
Abstract: …and GPUs are separated devices connected at low data transfer rate and high data transfer latency. Emerging computing chips tend to integrate CPUs and GPUs. One example is the Apple silicon chips with unified memory. Such unified memory chips have opened doors for new strategies of collaboration between CPUs and GPUs f… ▽ More
Submitted 17 March, 2024; v1 submitted 21 January, 2024; originally announced January 2024.
11 arXiv:2306.16391
Uncovering Software-Based Power Side-Channel Attacks on Apple M1/M2 Systems
Authors: Nikhil Chawla, Chen Liu, Abhishek Chakraborty, Igor Chervatyuk, Ke Sun, Thais Moreira Hamasaki, Henrique Kawakami
Abstract: …interface might be used for power side-channel attacks without physical access. In this paper, we show that such software-based power side-channel attack is also applicable on Apple…
Submitted 4 October, 2024; v1 submitted 28 June, 2023; originally announced June 2023.
12 arXiv:2211.00720
Apple Silicon Performance in Scientific Computing
Authors: Connor Kenyon, Collin Capano
Abstract: With the release of the Apple Silicon System-on-a-Chip processors, and the impressive performance shown in general use by both the M1 and M1 Ultra, the potential use for Apple Silicon processors in scientific computing is explored. Both the… ▽ More
Submitted 1 November, 2022; originally announced November 2022.
Comments: 10 pages, 3 figures, IEEE HPEC
13 arXiv:2104.12910
doi10.1109/MRA.2021.3128367
A RoboStack Tutorial: Using the Robot Operating System Alongside the Conda and Jupyter Data Science Ecosystems
Authors: Tobias Fischer, Wolf Vollprecht, Silvio Traversaro, Sean Yen, Carlos Herrero, Michael Milford
Abstract: …and Galactic) can run simultaneously on one machine, with pre-compiled binaries available for Linux, Windows and OSX, and the ARM architecture (e.g. the Raspberry Pi and the new Apple Silicon). To deal with the large size of the ROS ecosystem, we significantly improved the speed of the Conda solver and build system by…
Submitted 15 December, 2021; v1 submitted 26 April, 2021; originally announced April 2021.
14 arXiv:2506.03870 Evaluating Apple Intelligence's Writing Tools for Privacy Against Large Language Model-Based Inference Attacks: Insights from Early Datasets
Authors: Mohd. Farhan Israk Soumik, Syed Mhamudul Hasan, Abdur R. Shahid
Abstract: …infer emotions from text for malicious purposes, known as emotion inference attacks, poses a significant threat to user privacy. In this paper, we investigate the potential of Apple… ▽ More
Submitted 4 June, 2025; originally announced June 2025.
15 arXiv:2502.18527 GOD model: Privacy Preserved AI School for Personal Assistant
Authors: PIN AI Team, Bill Sun, Gavin Guo, Regan Peng, Boliang Zhang, Shouqiao Wang, Laura Florescu, Xi Wang, Davide Crapis, Ben Wu
Abstract: Personal AI assistants (e.g., Apple Intelligence, Meta AI) offer proactive recommendations that simplify everyday tasks, but their reliance on sensitive user data raises concerns about privacy and trust. To address these challenges, we introduce the Guardian of Data (GOD), a secure, privacy-preserving framework for tra… ▽ More
Submitted 27 February, 2025; v1 submitted 24 February, 2025; originally announced February 2025.
16 arXiv:2407.21075 Apple Intelligence Foundation Language Models
Authors: Tom Gunter, Zirui Wang, Chong Wang, Ruoming Pang, Andy Narayanan, Aonan Zhang, Bowen Zhang, Chen Chen, Chung-Cheng Chiu, David Qiu, Deepak Gopinath, Dian Ang Yap, Dong Yin, Feng Nan, Floris Weers, Guoli Yin, Haoshuo Huang, Jianyu Wang, Jiarui Lu, John Peebles, Ke Ye, Mark Lee, Nan Du, Qibin Chen, Quentin Keunebroek , et al. (130 additional authors not shown)
Abstract: We present foundation language models developed to power Apple Intelligence features, including a ~3 billion parameter model designed to run efficiently on devices and a large server-based language model designed for Private Cloud Compute. These models are designed to perform a wide range of tasks efficiently, accurate… ▽ More
Submitted 29 July, 2024; originally announced July 2024.
課題研究
https://qiita.com/kaizen_nagoya/items/cb41441cf520c68b13bc
職業訓練(IT)
https://qiita.com/kaizen_nagoya/items/95368b63fa21d64271ec