Machine Learning Hardware Applications

Intel presents the first SYCL language implementation of massively accelerated fully-fused multi-layer perceptrons (MLPs) applied on Intel GPUs that support Intel XMX instructions and an open-sourced repository of the implementation. In addition to high performance computing, MLP acceleration libraries offer compatibility with PyTorch, versatile neural network structures, multi-resolution hash encoding, and cross-platform utilization. These types of networks are the foundation of applications like neural radiance Fields (NeRFs) and physics-informed neural networks (PINNs) for fluid mechanics. Intel's approach focuses on maximizing data reuse within the general register file and the shared local memory, minimizing the need for slower global memory accesses. This results in a significant increase in the arithmetic intensity, which leads to improved performance. The paper shows examples of how MLPs demonstrate higher performance on four applications: regression benchmark, image compression, NeRFs, and PINNs. The paper shows a performance improvement up to 1.75x for training and 2.84x for inference over another fully-fused implementation, and a performance increase of a factor up to 30 over off-the-shelf PyTorch implementations. Fully-fused MLPs applications include identification in vision and NLP, prediction in social media and biochemistry, and reinforcement learning in robotics. Read the blog: https://lnkd.in/dDUABa7q Read the paper: https://lnkd.in/d_5n3Sn8 View the code: https://lnkd.in/d4u8PvGJ Build the library: https://lnkd.in/d4E46BYA

Using evolutionary programming with OpenEvolve (my open-source implementation of DeepMind's AlphaEvolve), I successfully optimized Metal kernels for transformer attention on Apple Silicon, achieving 12.5% average performance improvements with 106% peak speedup on specific workloads. What makes this particularly exciting: 🔬 No human expert provided GPU programming knowledge - the system autonomously discovered hardware-specific optimizations including perfect SIMD vectorization for Apple Silicon and novel algorithmic improvements like two-pass online softmax 📊 Comprehensive evaluation across 20 diverse inference scenarios showed workload-dependent performance with significant gains on dialogue tasks (+46.6%) and extreme-length generation (+73.9%), though some regressions on code generation (-16.5%) ⚡ The system discovered genuinely novel optimizations: 8-element vector operations that perfectly match Apple Silicon's capabilities, memory access patterns optimized for Qwen3's 40:8 grouped query attention structure, and algorithmic innovations that reduce memory bandwidth requirements 🎯 This demonstrates that evolutionary code optimization can compete with expert human engineering, automatically discovering hardware-specific optimizations that would require deep expertise in GPU architecture, Metal programming, and attention algorithms The broader implications are significant. As hardware architectures evolve rapidly (new GPU designs, specialized AI chips), automated optimization becomes invaluable for discovering optimizations that would be extremely difficult to find manually. This work establishes evolutionary programming as a viable approach for automated GPU kernel discovery with potential applications across performance-critical computational domains. All code, benchmarks, and evolved kernels are open source and available for the community to build upon. The technical write-up with complete methodology and results is published on Hugging Face. The intersection of evolutionary algorithms and systems optimization is just getting started. Links in first comment 👇 #AI #MachineLearning #GPUOptimization #PerformanceEngineering #OpenSource #EvolutionaryAlgorithms #AppleSilicon #TransformerOptimization #AutomatedProgramming

Over the past few years, edge AI on microcontrollers (often called TinyML) has quietly moved beyond demos and conference talks into real products. While it hasn’t seen the explosive visibility of cloud AI or large language models, the embedded industry is steadily adopting machine learning. My latest blog post looks at what that reality actually looks like in 2026: the typical workflows, the rise of vendor-specific AI toolchains, the role of open runtimes, and how silicon trends like small accelerators and Ethos-U integrations are expanding what’s practical without eliminating fundamental constraints. It also explores how edge AI is gaining traction in real applications, from predictive maintenance and agriculture to smart homes and autonomous systems, along with a brief look at ongoing research such as compute-in-memory and neuromorphic computing. The takeaway is simple: edge AI isn’t replacing embedded engineers or domain expertise anytime soon. Instead, it’s growing and evolving to solve real-world problems by providing professional-level hardware and toolchains. Check out the full post here: https://lnkd.in/gbikRUb6 #EdgeAI #TinyML #AI #embedded #microcontroller #programming

Machine Learning (ML) offers a unique way to prioritize the ASIC Verification task by bringing in automation and other flows to meet the market demands of the chip design. Some of the applications of ML include, but are not limited to, automated detection of unexpected behaviour of the design, predicting regression failures, bucketizing the failures, and filling the coverage holes by analyzing the data and patterns. The indicated ML-assisted flow looks like this: Data Collection --> Feature Extraction --> Model training ( PyTorch, scikit-learn, Tensorflow, Keras, Weights and biases) --> Feedback --> Inference and Insight. Applications of Machine Learning: [1] Anomaly Detection: Unsupervised models (Autoencoders, clustering) spot rare timing violations or glitch pattern. [2] Failure Prediction: Supervised classifiers rank testcases by failure probability. [3] Coverage Hole Identification: Dimensionality reduction (PCA, t-SNE) visualizes untested corner cases and guides the generation of new stimuli. [4] Testbench optimization: Reinforcement learning algorithms adapt stimulus generators to maximize functional coverage with fewer cycles. [5] Automated Assertion (SVA) Generation from Spec: Microarchitecture specification is the starting point for Design Architecture and Test Planning. There are tools already that use progressive regularization and post-processing to convert prompts written in English, extracted from the spec, into Assertions. All these are used using an LLM interface. #vlsi #asic #electricalengineering #MachineLearning

Day 26 examines the specialized hardware that accelerates AI computations, enabling the rapid training and inference of complex models. Key Hardware Components: * TPUs (Tensor Processing Units): Custom-built by Google, these processors are optimized for matrix multiplications and feature high-bandwidth memory (HBM). * GPUs: NVIDIA’s GPUs, such as the A100 and H100, combine CUDA cores with tensor cores to deliver exceptional performance for AI tasks. * Neuromorphic Chips: IBM’s TrueNorth is an example of chips designed to mimic the human brain’s neural structure. For instance, NVIDIA’s H100 GPU offers up to 4 TB/s memory bandwidth and 800 TOPS (trillions of operations per second) for INT8 computations, along with a dedicated transformer engine for mixed-precision arithmetic. Large-scale inference for models like ChatGPT relies on thousands of GPUs (often A100s) with highly optimized kernels. This hardware underpins rapid response times in production environments. How might emerging technologies like photonic computing further revolutionize AI hardware? #AIHardware #ChipDesign #HighPerformanceComputing

Understanding the Engine Behind Today's AI Advances: Cloud, GPUs, and Business Impact As we navigate mid-April 2025, the demands of Artificial Intelligence are rapidly evolving. Training sophisticated models and processing vast datasets requires immense computational power, moving beyond the capabilities of traditional infrastructure. This is driving a crucial convergence: the scalability of cloud platforms combined with the specialized processing power of Graphics Processing Units (GPUs). Google Cloud serves as a prime example of this synergy. By integrating high-performance GPUs (like NVIDIA's H100 and L4 Tensor Core GPUs) directly into its scalable infrastructure, often orchestrated via platforms like Vertex AI, it provides the necessary engine for modern AI workloads. The massively parallel architecture of GPUs is particularly well-suited for the complex matrix calculations fundamental to deep learning. What does this technological convergence enable in practice? 📈 Accelerated Development & Research: We're observing substantial reductions in the time needed to train complex AI models. Reports often indicate significant generational improvements (e.g., up to 9x faster for certain workloads compared to previous GPU generations), drastically shortening development cycles from weeks or months to days or hours. This allows for more rapid experimentation and refinement. 🔓 Expanded Scope for Complex Problems: This accessible compute power makes tackling previously prohibitive tasks increasingly feasible. This includes training massive foundation models (with trillions of parameters), running intricate scientific simulations, and developing sophisticated generative AI applications across various industries. 📊 Enhanced Real-Time Capabilities: The ability to efficiently run trained models (inference) on cloud GPUs enables applications requiring immediate insights or interactions. This is crucial for areas like dynamic customer personalization, real-time analytics, and advanced operational monitoring. This combination of scalable cloud resources and powerful, specialized hardware like GPUs is becoming fundamental for organizations aiming to leverage AI effectively. It's less about hype and more about the enabling infrastructure required for the next wave of data-driven strategies, relevant globally and certainly within the evolving tech landscape here in Mexico. It's interesting to see the diverse applications emerging from this accelerated computing power. What are the most compelling use cases or industry impacts you're observing? Share your perspective below. #GoogleCloud #AI #ArtificialIntelligence #GPU #MachineLearning #CloudComputing #TechTrends #DataScience #VertexAI #NVIDIA #H100 #DeepLearning #HPC #AIinfrastructure #TransformacionDigital