Spiral AI/ML: Co-Optimization for High-Performance, Data-Intensive Computing in Resource Constrained Environments

Abstract

Problem(s): Increasing complexity in computing architectures; Mission cost, size, weight, and power (CSWAP) constraints drive increasing use of FPGAs and ASICs (more complexity); Achieving performance from these platforms is hard; Achieving performance from data-intensive applications (graphs, ML, AI) is hard. Solution: Automatic code generation for data-intensive computations; Simultaneous, automatic co-optimization of hardware within CSWAP constraints. Approach: Identify common AI/ML/Graph computational primitives. Encode knowledge about graph, ML, and AI computational primitives into Spiral code-gen technology; Develop hardware performance models allowing Spiral to choose between components satisfying CSWAP requirements.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2019
Accession Number
AD1118561

Entities

People

  • Franz Franchetti
  • James C. Hoe
  • Scott McMillan
  • Tze M. Low

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Big Data
  • Computations
  • Computer Architecture
  • Computing System Architectures
  • Data Analysis
  • Demographic Cohorts
  • Department Of Defense
  • Floating Point Operations
  • Guarantees
  • Intelligent Agents
  • Learning
  • Linear Algebra
  • Machine Learning
  • Materials
  • Optimization
  • Platforms
  • Predictive Analytics
  • Software Development
  • Sparse Matrix
  • Specifications
  • Universities

Fields of Study

  • Computer science

Readers

  • Computational Linguistics
  • Distributed Systems and Data Platform Development