Blog posts discussing the technical implementation detail "Dialect"
← Back to all tagsBlog posts discussing the technical implementation detail "Dialect"
← Back to all tagsThe blog post “Abstract Machine Models - Also: what Rust got particularly right” makes a compelling case for Abstract Machine Models (AMMs) as a missing conceptual layer between computer science and hardware. The author, reflecting on a failed microprocessor project, discovers that programmers don’t reason about either programming theory or raw hardware, but rather about intermediate mental models that predict extra-functional behavior: execution time, memory usage, concurrency patterns, energy consumption. These AMMs, the author argues, exist independently of both languages and hardware, explaining how a C programmer can transfer skills to Python despite their semantic differences.
Read MoreThe “AI industrial complex” in its current form is not sustainable. While transformers have delivered remarkable capabilities, their energy consumption and computational demands reveal a fundamental inefficiency: we’re fighting against nature’s design principles. The human brain operates on roughly 20 watts, processing massive volumes of information through sparse, event-driven spikes. (at least, as we currently understand it today) Current AI systems consume thousands of watts to support narrow inference capabilities, forcing dense matrix operations through every computation.
Read MoreIn 1998, Andrew Appel published a paper that heralded a change to how we should think about compiler design. “SSA is Functional Programming” demonstrated that Static Single-Assignment form, the intermediate representation at the heart of modern optimizing compilers, is exactly equivalent to functional programming with nested lexical scope. This insight has profound implications as we enter a new era of hardware-software co-design. At SpeakEZ, this revelation validates our approach with the Fidelity framework more than 25 years after its first publication: lowering F# to native code through MLIR isn’t just possible, it’s aligned to the fundamental structure of well-principled compilation.
Read MoreSpeakEZ’s Fidelity framework with its innovative BAREWire technology is uniquely positioned to take advantage of emerging memory coherence and interconnect technologies like CXL, NUMA, and recent PCIe enhancements. By combining BAREWire’s zero-copy architecture with these hardware innovations, Fidelity can put the developer in unprecedented control over heterogeneous computing environments with the elegant semantics of a high-level language. This innovation represents a fundamental shift in how distributed memory systems interact, and the cognitive demands it places on the software engineering process.
Read MoreFor .NET developers, the term “frontend” already carries rich meaning. It might evoke XAML-based technologies like WPF or UWP, the hybrid approach of Blazor, or perhaps JavaScript visualization frameworks such as Angular, Vue or React. Within the .NET ecosystem, “frontend” generally refers to user interface technologies - the presentation layer of applications. When that same .NET developer encounters terminology like “MLIR C/C++ Frontend Working Group,” something doesn’t quite compute. This clearly isn’t referring to user interfaces or presentation technologies.
Read MoreThe computing world has fragmented into specialized ecosystems - embedded systems demand byte-level control, mobile platforms enforce strict resource constraints, while server applications require elasticity and parallelism. Traditionally, these environments have forced developers to choose between conflicting approaches: use a high-level language with garbage collection and accept the performance overhead, or drop down to systems programming with manual memory management and lose expressiveness. Beyond Runtime Boundaries The Fidelity Framework represents a fundamental rethinking of this dichotomy.
Read MoreIn the world of artificial intelligence, a quiet revolution is taking place. For more than a decade, the presumed fundamental building block of neural networks has been matrix multiplication (or “matmul” in industry parlance) – the mathematical operation that powers everything from language models like ChatGPT to computer vision systems analyzing medical images. But what if we told you that matrix multiplication, the cornerstone of current AI, is actually a significant bottleneck for efficiency?
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