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GitHub - trevorpogue/algebraic-nnhw: AI acceleration using matrix multiplication with half the multiplications (github.com)

AI Acceleration machine learning Algorithm architecture Source Code AI and Machine Learning Artificial Intelligence

This GitHub repository presents transformative advancements in machine learning accelerator architectures through a novel algorithm, the Free-pipeline Fast Inner Product (FFIP), which demands nearly half the number of multiplier units for equivalent performance, trading multiplications for low-bitwidth additions. It includes complete source code for implementing the FFIP algorithm and architecture, aimed at enhancing the computational efficiency of ML accelerators.
Main Points
- FFIP Algorithm and ArchitectureThe repository delivers a novel algorithm (FFIP) alongside a hardware architecture that enhances the compute efficiency of ML accelerators by reducing the number of necessary multiplications.
- Applicability and Performance of FFIPThe FFIP algorithm is applicable across various machine learning model layers and has been shown to outperform existing solutions in throughput and compute efficiency.
- Comprehensive Source Code for ImplementationThe source code provides a comprehensive setup for implementation including a compiler, RTL descriptions, simulation scripts, and testbenches.
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Designing Concurrent Systems on the BEAM (happihacking.com)

development Erlang Elixir architecture design processes BEAM Concurrency Actor model system design maintainability Technology

Designing concurrent systems on the BEAM involves leveraging its concurrency model, rooted in the actor model, for process isolation and communication. A fundamental understanding that processes are not equated with code, and visualization strategies like considering processes as workers, are pivotal for system design. Key strategies include clear task allocation, flow-based structuring, and understanding process archetypes. Structuring code according to business domains further aligns technical solutions with business strategy, promoting maintainable and scalable system architecture.
Main Points
- Overview of BEAM's Concurrency ModelThe BEAM's concurrency model, rooted in the actor model, focuses on lightweight, isolated processes communicating through message passing, fostering system resilience and maintainability.
- Clearing Misconceptions: Processes ≠ CodeMisinterpretation of processes as code can obscure the understanding of BEAM's concurrency model, but recognizing their separation illuminates design considerations for concurrency.
- Visualizing Processes for Better UnderstandingPersonifying processes as gnomes offers a tangible understanding of their independent operation, state management, and communication, enriching the conceptual grasp of concurrent system design on BEAM.
- Key Design Strategies for Concurrent SystemsTask allocation based on responsibility, structuring systems around data flow, and categorizing processes according to specific archetypes are critical strategies for designing concurrent systems on BEAM.
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