Blog posts related to the .NET/F# concept "Units-of-Measure"
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← Back to all tagsA Confession and a Vision A personal note from the founder of SpeakEZ Technolgies, Houston Haynes I must admit something upfront: when I began design of the Fidelity framework in 2020, I was driven by practical engineering frustrations, particularly with AI development. The limitations of a managed runtime, the endless battle with numeric precision, machine learning framework quirks, constant bug chasing; these weren’t just inconveniences, they felt like fundamental architectural flaws.
Read MoreThe computing industry stands at a fascinating juncture in 2025. After decades of general-purpose processor dominance that led to the accidental emergence of general purpose GPU, we’re witnessing what appears to be a reverse inflection point. Specialized architectures are re-emerging as an economic imperative, but with crucial differences from the LISP machines of the past. Our analysis examines how languages inheriting from LISP’s legacy, particularly F# and others with lineage to OCaml and StandardML, are uniquely positioned to realize the advantages of new hardware coming from vendors like NextSilicon, Groq, Cerebras and Tenstorrent: a concept we’re calling Dataflow Graph Architecture (DGA).
Read MoreA startup’s gene analysis samples nearly melted because someone confused Fahrenheit and Celsius in their monitoring system. A Mars orbiter was lost because of mixed metric and imperial units. Medication dosing errors have killed patients due to milligrams versus micrograms confusion. These aren’t edge cases - they’re symptoms of a fundamental problem in how we build mission-critical systems: Most languages approach types as an afterthought rather than a first line of defense.
Read MoreWhile this idea might be met with controversy in the current swarm of AI hype, we believe that the advent of sub-quadratic AI models, heterogeneous computing, and unified memory architectures will show themselves as pivotal components to next generation AI system design. The elements are certainly taking shape. As we stand at this technological crossroads, AMD’s evolving unified CPU/GPU architecture, exemplified by the MI300A and its planned successors (MI325, MI350, MI400), combined with their strategic acquisition of Xilinx, offers a compelling case study for re-imagining how AI models can operate.
Read MoreThe future of AI inference lies not in ever-larger transformer models demanding massive GPU clusters, but in a diverse ecosystem of specialized architectures optimized for specific deployment scenarios. At SpeakEZ, we’re developing the infrastructure that could make this future a reality. While our “Beyond Transformers” analysis explored the theoretical foundations of matmul-free and sub-quadratic models, this article outlines how our Fidelity Framework could transform these innovations into practical, high-performance inference systems that would span from edge devices to distributed data centers.
Read MoreAs a companion to our exploration of CXL and memory coherence, this article examines how the Fidelity framework could extend its zero-copy paradigm beyond single-system boundaries. While our BAREWire protocol is designed to enable high-performance, zero-copy communication within a system, modern computing workloads often span multiple machines or data centers. Remote Direct Memory Access (RDMA) technologies represent a promising avenue for extending BAREWire’s zero-copy semantics across network boundaries. This planned integration of RDMA capabilities with BAREWire’s memory model would allow Fidelity to provide consistent zero-copy semantics from local processes all the way to cross-datacenter communication, expressed through F#’s elegant functional programming paradigm.
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 MoreThe computing landscape stands at an inflection point. AI accelerators are reshaping our expectations of performance while “quantum” looms as both opportunity for and threat to our future. Security vulnerabilities in memory-unsafe code continue to cost billions annually. Yet the vast ecosystem of foundational libraries, from TensorFlow’s core implementations to OpenSSL, remains anchored in C and C++. How might we bridge this chasm between the proven code we depend on and the type-safe, accelerated future we’re building at an increasing pace?
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 MoreAt SpeakEZ, we are working on transformative approaches to transfer learning that combine convolutional neural networks (CNNs) with Topological Object Classification (TopOC) methods. This memo outlines our design approach to creating dimensionally-constrained models that maintain representational integrity throughout the transfer learning process while enabling deployment to resource-constrained hardware through our Fidelity Framework compilation pipeline. By leveraging F#’s Units of Measure (UMX) system to enforce dimensional constraints across the entire model architecture, we achieve not only safer and more reliable models but also significantly more efficient computational patterns that can be directly compiled to FPGAs and custom ASICs.
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?
Read MoreThe AI industry is experiencing a profound shift in how computational resources are allocated and optimized. While the last decade saw rapid advances through massive pre-training efforts on repurposed GPUs, we’re now entering an era where test-time compute (TTC) and custom accelerators are emerging as the next frontier of AI advancement. As highlighted in recent industry developments, DeepSeek AI lab disrupted the market with a model that delivers high performance at a fraction of competitors’ costs, signaling two significant shifts: smaller labs producing state-of-the-art models and test-time compute becoming the next driver of AI progress.
Read MoreIn the world of distributed systems, trust is fundamentally a mathematical problem. For decades, organizations have relied on single points of failure: a master key, a root certificate, a privileged administrator. But what if we told you that the mathematics of secure multi-party computation, pioneered by Adi Shamir in 1979 and refined through Schnorr signatures, has reached a point where distributed trust is not just theoretically possible, but practically superior to centralized approaches?
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