Search Results for "cpu chip"

x
Sort By:

Showing 2 open source projects for "cpu chip"

View related business solutions
Apple iPhone Clear Filters & Widen Search
  • Our Free Plans just got better! | Auth0 Icon
    Our Free Plans just got better! | Auth0

    With up to 25k MAUs and unlimited Okta connections, our Free Plan lets you focus on what you do best—building great apps.

    You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
    Try free now
  • $300 Free Credits for Your Google Cloud Projects Icon
    $300 Free Credits for Your Google Cloud Projects

    Start building on Google Cloud with $300 in free credits. No commitment, no credit card required until you're ready to scale.

    Launch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
    Start Free Trial
  • 1

    Prime number ( primenumbers )

    Benchmark for 50 000 000 prime numbers as single and multicore

    Simple source files and compiled JAR Java programs, for benchmark 50 000 000 cycle finding prime numbers. On Intel(R) Core(TM) i5-8600K CPU, Windows 10 20H2, i have 39 second on single core and 7,6 second on multi core. (PS: C++ multicore 6 second). Added C files for gcc compiler in Windows 10 and for Xcode C command line project in MacOS ( tested on Mac mini M2 with single core 16 to 25 sec and multicore 2,3 to 5 second by compiler -O switch). Surprise, same code in JavaScript for M2 chip in Safari: 12,5 sec single core and 3,3 sec multi core. ...
    Downloads: 1 This Week
    Last Update:
    See Project
  • 2
    MACE

    MACE

    Deep learning inference framework optimized for mobile platforms

    Mobile AI Compute Engine (or MACE for short) is a deep learning inference framework optimized for mobile heterogeneous computing on Android, iOS, Linux and Windows devices. Runtime is optimized with NEON, OpenCL and Hexagon, and Winograd algorithm is introduced to speed up convolution operations. The initialization is also optimized to be faster. Chip-dependent power options like big.LITTLE scheduling, Adreno GPU hints are included as advanced APIs. UI responsiveness guarantee is sometimes...
    Downloads: 1 This Week
    Last Update:
    See Project
  • Previous
  • You're on page 1
  • Next

Related Searches

Related Categories

Auth0 Logo