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  • Continuum
  • Data
    • Datasets
      • Pre Training Data
      • Types of Fine Tuning
      • Self Instruct Paper
      • Self-Alignment with Instruction Backtranslation
      • Systematic Evaluation of Instruction-Tuned Large Language Models on Open Datasets
      • Instruction Tuning
      • Instruction Fine Tuning - Alpagasus
      • Less is More For Alignment
      • Enhanced Supervised Fine Tuning
      • Visualising Data using t-SNE
      • UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
      • Training and Evaluation Datasets
      • What is perplexity?
  • MODELS
    • Foundation Models
      • The leaderboard
      • Foundation Models
      • LLama 2 - Analysis
      • Analysis of Llama 3
      • Llama 3.1 series
      • Google Gemini 1.5
      • Platypus: Quick, Cheap, and Powerful Refinement of LLMs
      • Mixtral of Experts
      • Mixture-of-Agents (MoA)
      • Phi 1.5
        • Refining the Art of AI Training: A Deep Dive into Phi 1.5's Innovative Approach
      • Phi 2.0
      • Phi-3 Technical Report
  • Training
    • The Fine Tuning Process
      • Why fine tune?
        • Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?
        • Explanations in Fine Tuning
      • Tokenization
        • Tokenization Is More Than Compression
        • Tokenization - SentencePiece
        • Tokenization explore
        • Tokenizer Choice For LLM Training: Negligible or Crucial?
        • Getting the most out of your tokenizer for pre-training and domain adaptation
        • TokenMonster
      • Parameter Efficient Fine Tuning
        • P-Tuning
          • The Power of Scale for Parameter-Efficient Prompt Tuning
        • Prefix-Tuning: Optimizing Continuous Prompts for Generation
        • Harnessing the Power of PEFT: A Smarter Approach to Fine-tuning Pre-trained Models
        • What is Low-Rank Adaptation (LoRA) - explained by the inventor
        • Low Rank Adaptation (Lora)
        • Practical Tips for Fine-tuning LMs Using LoRA (Low-Rank Adaptation)
        • QLORA: Efficient Finetuning of Quantized LLMs
        • Bits and Bytes
        • The Magic behind Qlora
        • Practical Guide to LoRA: Tips and Tricks for Effective Model Adaptation
        • The quantization constant
        • QLORA: Efficient Finetuning of Quantized Language Models
        • QLORA and Fine-Tuning of Quantized Language Models (LMs)
        • ReLoRA: High-Rank Training Through Low-Rank Updates
        • SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models
        • GaLora: Memory-Efficient LLM Training by Gradient Low-Rank Projection
      • Hyperparameters
        • Batch Size
        • Padding Tokens
        • Mixed precision training
        • FP8 Formats for Deep Learning
        • Floating Point Numbers
        • Batch Size and Model loss
        • Batch Normalisation
        • Rethinking Learning Rate Tuning in the Era of Language Models
        • Sample Packing
        • Gradient accumulation
        • A process for choosing the learning rate
        • Learning Rate Scheduler
        • Checkpoints
        • A Survey on Efficient Training of Transformers
        • Sequence Length Warmup
        • Understanding Training vs. Evaluation Data Splits
        • Cross-entropy loss
        • Weight Decay
        • Optimiser
        • Caching
      • Training Processes
        • Extending the context window
        • PyTorch Fully Sharded Data Parallel (FSDP)
        • Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation
        • YaRN: Efficient Context Window Extension of Large Language Models
        • Sliding Window Attention
        • LongRoPE
        • Reinforcement Learning
        • An introduction to reinforcement learning
        • Reinforcement Learning from Human Feedback (RLHF)
        • Direct Preference Optimization: Your Language Model is Secretly a Reward Model
  • INFERENCE
    • Why is inference important?
      • Grouped Query Attention
      • Key Value Cache
      • Flash Attention
      • Flash Attention 2
      • StreamingLLM
      • Paged Attention and vLLM
      • TensorRT-LLM
      • Torchscript
      • NVIDIA L40S GPU
      • Triton Inference Server - Introduction
      • Triton Inference Server
      • FiDO: Fusion-in-Decoder optimised for stronger performance and faster inference
      • Is PUE a useful measure of data centre performance?
      • SLORA
  • KNOWLEDGE
    • Vector Databases
      • A Comprehensive Survey on Vector Databases
      • Vector database management systems: Fundamental concepts, use-cases, and current challenges
      • Using the Output Embedding to Improve Language Models
      • Decoding Sentence-BERT
      • ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
      • SimCSE: Simple Contrastive Learning of Sentence Embeddings
      • Questions Are All You Need to Train a Dense Passage Retriever
      • Improving Text Embeddings with Large Language Models
      • Massive Text Embedding Benchmark
      • RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking
      • LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
      • Embedding and Fine-Tuning in Neural Language Models
      • Embedding Model Construction
      • Demystifying Embedding Spaces using Large Language Models
      • Fine-Tuning Llama for Multi-Stage Text Retrieval
      • Large Language Model Based Text Augmentation Enhanced Personality Detection Model
      • One Embedder, Any Task: Instruction-Finetuned Text Embeddings
      • Vector Databases are not the only solution
      • Knowledge Graphs
        • Harnessing Knowledge Graphs to Elevate AI: A Technical Exploration
        • Unifying Large Language Models and Knowledge Graphs: A Roadmap
      • Approximate Nearest Neighbor (ANN)
      • High Dimensional Data
      • Principal Component Analysis (PCA)
      • Vector Similarity Search - HNSW
      • FAISS (Facebook AI Similarity Search)
      • Unsupervised Dense Retrievers
    • Retrieval Augmented Generation
      • Retrieval-Augmented Generation for Large Language Models: A Survey
      • Fine-Tuning or Retrieval?
      • Revolutionising Information Retrieval: The Power of RAG in Language Models
      • A Survey on Retrieval-Augmented Text Generation
      • REALM: Retrieval-Augmented Language Model Pre-Training
      • Retrieve Anything To Augment Large Language Models
      • Generate Rather Than Retrieve: Large Language Models Are Strong Context Generators
      • Active Retrieval Augmented Generation
      • DSPy: LM Assertions: Enhancing Language Model Pipelines with Computational Constraints
      • DSPy: Compiling Declarative Language Model Calls
      • DSPy: In-Context Learning for Extreme Multi-Label Classification
      • Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs
      • HYDE: Revolutionising Search with Hypothetical Document Embeddings
      • Enhancing Recommender Systems with Large Language Model Reasoning Graphs
      • Retrieval Augmented Generation (RAG) versus fine tuning
      • RAFT: Adapting Language Model to Domain Specific RAG
      • Summarisation Methods and RAG
      • Lessons Learned on LLM RAG Solutions
      • Stanford: Retrieval Augmented Language Models
      • Overview of RAG Approaches with Vector Databases
      • Mastering Chunking in Retrieval-Augmented Generation (RAG) Systems
    • Semantic Routing
    • Resource Description Framework (RDF)
  • AGENTS
    • What is agency?
      • Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves
      • Types of Agents
      • The risk of AI agency
      • Understanding Personality in Large Language Models: A New Frontier in AI Psychology
      • AI Agents - Reasoning, Planning, and Tool Calling
      • Personality and Brand
      • Agent Interaction via APIs
      • Bridging Minds and Machines: The Legacy of Newell, Shaw, and Simon
      • A Survey on Language Model based Autonomous Agents
      • Large Language Models as Agents
      • AI Reasoning: A Deep Dive into Chain-of-Thought Prompting
      • Enhancing AI Reasoning with Self-Taught Reasoner (STaR)
      • Exploring the Frontier of AI: The "Tree of Thoughts" Framework
      • Toolformer: Revolutionising Language Models with API Integration - An Analysis
      • TaskMatrix.AI: Bridging Foundational AI Models with Specialised Systems for Enhanced Task Completion
      • Unleashing the Power of LLMs in API Integration: The Rise of Gorilla
      • Andrew Ng's presentation on AI agents
      • Making AI accessible with Andrej Karpathy and Stephanie Zhan
  • Regulation and Ethics
    • Regulation and Ethics
      • Privacy
      • Detecting AI Generated content
      • Navigating the IP Maze in AI: The Convergence of Blockchain, Web 3.0, and LLMs
      • Adverse Reactions to generative AI
      • Navigating the Ethical Minefield: The Challenge of Security in Large Language Models
      • Navigating the Uncharted Waters: The Risks of Autonomous AI in Military Decision-Making
  • DISRUPTION
    • Data Architecture
      • What is a data pipeline?
      • What is Reverse ETL?
      • Unstructured Data and Generatve AI
      • Resource Description Framework (RDF)
      • Integrating generative AI with the Semantic Web
    • Search
      • BM25 - Search Engine Ranking Function
      • BERT as a reranking engine
      • BERT and Google
      • Generative Engine Optimisation (GEO)
      • Billion-scale similarity search with GPUs
      • FOLLOWIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions
      • Neural Collaborative Filtering
      • Federated Neural Collaborative Filtering
      • Latent Space versus Embedding Space
      • Improving Text Embeddings with Large Language Models
    • Recommendation Engines
      • On Interpretation and Measurement of Soft Attributes for Recommendation
      • A Survey on Large Language Models for Recommendation
      • Model driven recommendation systems
      • Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations
      • Foundation Models for Recommender Systems
      • Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review
      • AI driven recommendations - harming autonomy?
    • Logging
      • A Taxonomy of Anomalies in Log Data
      • Deeplog
      • LogBERT: Log Anomaly Detection via BERT
      • Experience Report: Deep Learning-based System Log Analysis for Anomaly Detection
      • Log-based Anomaly Detection with Deep Learning: How Far Are We?
      • Deep Learning for Anomaly Detection in Log Data: A Survey
      • LogGPT
      • Adaptive Semantic Gate Networks (ASGNet) for log-based anomaly diagnosis
  • Infrastructure
    • The modern data centre
      • Enhancing Data Centre Efficiency: Strategies to Improve PUE
      • TCO of NVIDIA GPUs and falling barriers to entry
      • Maximising GPU Utilisation with Kubernetes and NVIDIA GPU Operator
      • Data Centres
      • Liquid Cooling
    • Servers and Chips
      • The NVIDIA H100 GPU
      • NVIDIA H100 NVL
      • Lambda Hyperplane 8-H100
      • NVIDIA DGX Servers
      • NVIDIA DGX-2
      • NVIDIA DGX H-100 System
      • NVLink Switch
      • Tensor Cores
      • NVIDIA Grace Hopper Superchip
      • NVIDIA Grace CPU Superchip
      • NVIDIA GB200 NVL72
      • Hopper versus Blackwell
      • HGX: High-Performance GPU Platforms
      • ARM Chips
      • ARM versus x86
      • RISC versus CISC
      • Introduction to RISC-V
    • Networking and Connectivity
      • Infiniband versus Ethernet
      • NVIDIA Quantum InfiniBand
      • PCIe (Peripheral Component Interconnect Express)
      • NVIDIA ConnectX InfiniBand adapters
      • NVMe (Non-Volatile Memory Express)
      • NVMe over Fabrics (NVMe-oF)
      • NVIDIA Spectrum-X
      • NVIDIA GPUDirect
      • Evaluating Modern GPU Interconnect
      • Scalable Hierarchical Aggregation and Reduction Protocol (SHARP)
      • Next-generation networking in AI environments
      • NVIDIA Collective Communications Library (NCCL)
    • Data and Memory
      • NVIDIA BlueField Data Processing Units (DPUs)
      • Remote Direct Memory Access (RDMA)
      • High Bandwidth Memory (HBM3)
      • Flash Memory
      • Model Requirements
      • Calculating GPU memory for serving LLMs
      • Transformer training costs
      • GPU Performance Optimisation
    • Libraries and Complements
      • NVIDIA Base Command
      • NVIDIA AI Enterprise
      • CUDA - NVIDIA GTC 2024 presentation
      • RAPIDs
      • RAFT
    • Vast Data Platform
      • Vast Datastore
      • Vast Database
      • Vast Data Engine
      • DASE (Disaggregated and Shared Everything)
      • Dremio and VAST Data
    • Storage
      • WEKA: A High-Performance Storage Solution for AI Workloads
      • Introduction to NVIDIA GPUDirect Storage (GDS)
        • GDS cuFile API
      • NVIDIA Magnum IO GPUDirect Storage (GDS)
      • Vectors in Memory
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On this page
  • Competitive Landscape
  • Future Prospects
  • Technology
  • Arm Holdings earnings call for Q4 of fiscal year 2024

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  1. Infrastructure
  2. Servers and Chips

ARM Chips

ARM Holdings is a leading semiconductor IP company that designs and licenses processor architectures and related technologies to semiconductor companies and OEMs worldwide.

ARM's business model is unique in the semiconductor industry, as they don't manufacture chips themselves but instead license their IP to others.

ARM, which initially stood for Advanced RISC Machines but now is simply a brand name, is a company that designs processor architectures used in a wide range of electronic devices.

ARM's product line-up is divided into several categories, each targeting specific applications and markets:

ARM Cortex-M series

These are 32-bit microcontroller CPU cores optimised for power efficiency and deterministic operation. They run at lower clock speeds and lack a memory management unit (MMU), making them unsuitable for running traditional operating systems like Windows, Linux, or Android. However, they are widely used in embedded systems, consumer electronics, and IoT devices.

ARM Cortex-A series

This series is designed for high-performance applications and is commonly found in smartphones, tablets, and other consumer devices.

The Cortex-A series has evolved from the 32-bit ARMv7 architecture to the 64-bit ARMv8 and ARMv9 architectures.

These processors often employ a heterogeneous multi-core design called "big.LITTLE," which combines high-performance cores with power-efficient cores to balance performance and battery life.

ARM Cortex-X series

Introduced in 2020, the Cortex-X series represents ARM's ultra-high-performance CPU cores.

These cores are designed to push the boundaries of single-threaded performance and are often paired with Cortex-A cores in a "1+3+4" or similar configuration, where one Cortex-X core is combined with three high-performance Cortex-A cores and four power-efficient Cortex-A cores.

ARM Neoverse series

Launched in 2018, the Neoverse series targets the server and infrastructure market.

These CPU cores are optimised for high core counts, scalability, and performance, making them suitable for use in data centres and cloud computing environments.

ARM's business model involves licensing its CPU core designs to semiconductor companies and OEMs. There are two main types of licenses:

Core license

This allows partners to use ARM's off-the-shelf CPU core designs, such as the Cortex-A55 or Cortex-X1, in their own SoCs (System on a Chip).

Architectural license

This license allows companies to design their own custom CPU cores based on the ARM architecture. Notable examples include Apple's A-series and M-series processors, and Qualcomm's Snapdragon X Elite.

To ensure compatibility and maintain a cohesive ecosystem, custom CPU core designs must pass a conformity test to guarantee 100% compatibility with the ARM architecture.

The history of ARM (Advanced RISC Machines)

The history of ARM (Advanced RISC Machines) is a fascinating journey that began in the 1980s and has led to its current position as one of the most influential and widely-used CPU architectures in the world.

Here's a detailed overview of ARM's history:

Origins at Acorn Computers (1983-1985): In 1983, Steve Furber and Sophie Wilson, two engineers at Acorn Computers in Cambridge, UK, began working on a new microprocessor design. Their goal was to create a low-power, high-performance chip that could be used in Acorn's next-generation computers. The resulting design, the Acorn RISC Machine (ARM), was first manufactured in 1985.

Apple's involvement and the formation of ARM Ltd. (1990): In 1990, Apple was looking for a low-power processor for its Newton PDA project. They chose the ARM chip and invested in Acorn, leading to the creation of a new company, Advanced RISC Machines Ltd. (ARM), as a joint venture between Acorn, Apple, and VLSI Technology.

Licensing model and early successes (1990s): ARM's unique business model involved licensing its processor designs to other companies, who could then manufacture chips based on the ARM architecture. This allowed ARM to focus on R&D while its partners handled manufacturing and sales. Early licensees included DEC, Intel, and Texas Instruments.

Dominance in the mobile market (2000s): As mobile devices like smartphones and tablets became increasingly popular, ARM's low-power, high-performance designs made it the go-to choice for manufacturers. Companies like Samsung, Qualcomm, and Apple (with its A-series chips) all used ARM-based processors in their devices.

Expansion into new markets (2010s): In the 2010s, ARM began to expand into new markets beyond mobile, including servers, networking equipment, and the Internet of Things (IoT). The company also introduced new architectures, such as ARMv8, which added 64-bit support.

Acquisition by SoftBank and proposed Nvidia merger (2016-2022): In 2016, Japanese conglomerate SoftBank acquired ARM for $32 billion. In 2020, Nvidia announced plans to acquire ARM from SoftBank for $40 billion, but the deal faced regulatory scrutiny and opposition from other tech companies. In February 2022, the merger was terminated due to these challenges.

Future developments and challenges: As of 2023, ARM remains a dominant force in the processor market, with its designs used in a wide range of devices from smartphones to servers. However, the company faces challenges from competitors like RISC-V and potential changes in its ownership structure.

Throughout its history, ARM has been characterised by its innovative processor designs, power-efficient architectures, and unique licensing model. These factors have contributed to its success and made it a key player in the global semiconductor industry.

Competitive Landscape

ARM's main competitors in the semiconductor IP market include Intel, MIPS (owned by Wave Computing), and RISC-V (an open-source instruction set architecture).

However, ARM has a dominant position in the mobile and embedded markets due to their low-power, high-performance designs.

ARM's market share in the smartphone market is around 90%, and they have a significant presence in other markets such as automotive, IoT, and embedded systems.

Future Prospects

ARM's future looks promising, driven by several factors:

Expansion into new markets

ARM is expanding into new markets such as data centres, automotive, and IoT. The recent announcements of ARM-based chips by Amazon (Graviton), Apple (M1), and Nvidia (Grace) for data centre and PC markets demonstrate ARM's potential beyond mobile.

Growth of AI and ML

The increasing demand for AI and machine learning workloads plays to ARM's strengths in energy efficiency and high performance. ARM's Neoverse platforms and the recent acquisition of Treasure Data position them well to capitalize on this trend.

Continued mobile growth

Despite the maturity of the smartphone market, ARM is well-positioned to benefit from the growth of 5G and the increasing sophistication of mobile devices.

Automotive and IoT

ARM's focus on low-power, high-performance designs makes them well-suited for the growing automotive and IoT markets, where energy efficiency is critical.

Technology

  1. Energy efficiency: ARM's designs are known for their low power consumption, which is critical in mobile and embedded applications where battery life is important.

  2. High performance: Despite their focus on energy efficiency, ARM's designs also deliver high performance, particularly in tasks that can be parallelised such as AI and ML workloads.

  3. Flexibility: ARM's IP-based business model allows their customers to customize and differentiate their products while still benefiting from the underlying ARM architecture.

  4. Ecosystem: ARM has a vast ecosystem of software, tools, and partners, which makes it easier for customers to develop and deploy ARM-based solutions.

Arm Holdings earnings call for Q4 of fiscal year 2024

Record Revenue Achievements: Arm reported record revenues for both the quarter and the fiscal year, with Q4 revenue up 47% year-over-year. This marks their first fiscal year as a public company, exceeding the high end of the guidance range.

Strong Growth in Royalties and Licensing: The revenue growth was primarily driven by a 37% year-over-year increase in royalties and a 60% increase in licensing revenue. This surge was attributed to the accelerated adoption of v9 architecture and increased R&D investments targeting AI applications.

v9 Architecture Adoption: There was significant growth in royalties, particularly due to the accelerated transition from v8 to v9 architecture, which not only improved royalties but also increased the number of CPUs per chip across various markets, particularly in smartphones.

Diversification and Strategic Partnerships: Arm noted strategic successes in diversifying its business, including a notable partnership with Google for the Axion processor designed for data centres, driven by compute efficiency and cost-effectiveness.

Advancements in Automotive and IoT: Arm introduced automotive-enhanced features with its v9 architecture for the automotive sector and launched the Ethos-U85, a low-power transformer for IoT-based designs.

Compute Subsystems Strategy: The company emphasised its strategy around compute subsystems (CSS), which involves integrating various IP blocks into a full solution. This approach has seen high demand, exceeding initial expectations, and is anticipated to drive significant growth.

AI's Impact on Growth: AI workloads, due to Arm's extensive CPU installation base, have become a significant driver of growth. The need for hardware to keep pace with rapidly advancing AI software has led to a substantial increase in licensing activity.

Financial Outlook: For FY 2025, Arm projects revenues between $3.8 billion to $4.1 billion, representing a 17% to 27% increase year-over-year, with non-GAAP operating expenses anticipated to be around $2.05 billion.

Market Share and Future Expectations: Arm expects continued revenue growth, maintaining at least a 20% increase annually for the subsequent years, driven by robust licensing demand and royalty growth from new and existing technologies.

These points underscore Arm's strong financial performance and strategic positioning in the semiconductor industry, particularly as it capitalizes on key trends like AI and data centre expansion.

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ARM's technology is based on the , which emphasises simplicity and efficiency. Their key technological advantages include:

reduced instruction set computing (RISC) architecture
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