As the inventor of Verilog, give a keynote at DAC about an ideal hardware design language based on TBC and Hexons that combines the syncronicity of SIGNAL with the statefulness of Erlang.ChatGPT Prompt (condensed) Opening: Setting the Stage Phil Good morning, everyone. It’s great to be here at DAC—a conference that brings together the best... Continue Reading →
WAVEs promise to redefine how we design, optimize, and deploy applications by tightly coupling software and hardware in ways previously unimaginable. With WAVEs, developers can create applications without worrying about hardware constraints, while the WAVE ensures the resulting design is perfectly mapped to hardware optimized for power, performance, and efficiency.
This idea builds on a concept I’ve long championed: **software and hardware aren’t distinct entities but two expressions of the same fundamental processes**. Hexons aim to reflect this by collapsing the boundary between the two, offering a new kind of computational atom that works equally well at the hardware and software levels.
The HLIR (High-Level Intermediate Representation) framework written in Homoiconic C could also serve as a next-generation replacement (“HLIR-NG”) for LLVM’s TableGen, especially if it’s designed to handle the kind of semantic richness and extensibility required for a dynamic, multi-level execution framework like MLIR.
instructions in a homoiconic form. It represents a novel synthesis in compiler design by bridging the gap between human and machine representations of programs. By combining monadic composition with homoiconic structure, HLIR allows developers to express computational intent with minimal syntax while maintaining direct mappings to MLIR's powerful optimization framework. This marriage of high-level semantics with low-level compilation produces a uniquely ergonomic intermediate representation - one where code is data, transformations are first-class citizens, and optimization becomes natural rather than imposed. The result is a language that is both easy for humans to reason about and efficient for compilers to transform, potentially setting a new standard for intermediate representations in modern compiler design.
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Prompt: What would a Littoral University designed from first principles around abundant computational intelligence differ from what we have today? The emergence of a Littoral University, grounded in AI-driven, interdisciplinary research and lifelong learning, would profoundly disrupt the traditional funding models of higher education. Tuition would move from degree-based payments to subscription and modular learning, catering to a diverse range of learners over their lifetimes. Research grants would shift from discipline-specific funding to problem-oriented and global collaborations, supported by AI’s ability to facilitate efficient, cross-disciplinary projects.
### Alan Turing Here’s a simple breakdown: **Shannon Machines:** - Start with data structures, with computation as secondary. - Focus on associative memory and managing state. - Use binary operators and bit transforms for math simulation. **Turing Machines:** - Start with basic arithmetic and build up to computation. - Provide a theoretical framework, independent of practical implementation. - Use algorithms to simulate any computing process.
By harnessing the strengths of LLMs for linguistic tasks, LNNs for perceptual and creative tasks, and SDMs for integration, the model promises to excel across a wide spectrum of cognitive tasks
Ambition Openness Risk Thankfulness Awareness
iHack, therefore iBlog 
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