• Skip to primary navigation
  • Skip to main content
  • Skip to primary sidebar

Engineering White Papers

White Papers, Catalogs, Case Studies and Resources for Engineers and Professionals

White Papers, Tech Notes, Catalogs and Case Studies

Search White Papers

  • 3D Printing
  • Fastening & Joining
  • Fluid Power
  • Electronics / EE
  • Motion Control
  • Networks
  • Software

Machine Learning at the Edge: Using HLS to Optimize Power and Performance

February 21, 2020 By

Moving machine learning to the edge has critical requirements on power and performance. Using off-the-shelf solutions is not practical. CPUs are too slow, GPUs/TPUs are expensive and consume too much power, and even generic machine learning accelerators can be overbuilt and are not optimal for power. In this paper, learn about creating new power/memory efficient hardware architectures to meet next-generation machine learning hardware demands at the edge.

Download this free white paper…

Filed Under: Siemens, White Papers

Primary Sidebar

Search White Papers

Categories

3D CAD 3D Printing 3D Systems Actuators Advanced Materials Ametek Analog Devices Inc. Anritsu Automation Bearings BWC Cables Catalogs Electronics / EE Encoders Faro Fastening & Joining Fluid Power Haydon Kerk HP Hydraulics Intel IoT Linear Motion Mechanical Medical Mentor Graphics Motion Control Motors National Instruments New White Papers Pneumatics Rapid Prototyping Renesas ROLLON Sensors Siemens Silicon Labs Solar Stratasys Tektronix Test & Measurement White Papers Wind Power Wireless

Copyright © 2025 · WTWH Media, LLC · Privacy Policy