Search Results for "android x86 iso"
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Showing 3 open source projects for "android x86 iso"
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Stop Cyber Threats with VM-Series Next-Gen Firewall on AzureGain integrated visibility across all traffic in a single pass. Deploy Palo Alto Networks VM-Series to determine application identity and content while automating security policy updates via rich APIs.
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Capstone
Capstone disassembly/disassembler framework
Capstone is a disassembly framework with the target of becoming the ultimate disasm engine for binary analysis and reversing in the security community. Created by Nguyen Anh Quynh, then developed and maintained by a small community, Capstone offers some unparalleled features. Support multiple hardware architectures: ARM, ARM64 (ARMv8), Ethereum VM, M68K, Mips, MOS65XX, PPC, Sparc, SystemZ, TMS320C64X, M680X, XCore and X86 (including X86_64). Having clean/simple/lightweight/intuitive... -
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Unicorn Engine
Unicorn CPU emulator framework (ARM, AArch64, M68K, Mips, Sparc
Unicorn is a lightweight multi-platform, multi-architecture CPU emulator framework. Highlight features. Multi-architectures: ARM, ARM64 (ARMv8), m68k, MIPS, PowerPC, RISC-V, S390x (SystemZ), SPARC, TriCore & x86 (include x86_64). Clean/simple/lightweight/intuitive architecture-neutral API. Implemented in pure C language, with bindings for Pharo, Crystal, Clojure, Visual Basic, Perl, Rust, Haskell, Ruby, Python, Java, Go, D, Lua, JavaScript, .NET, Delphi/Pascal & MSVC available. Native support for Windows & *nix (with macOS, Linux, Android, *BSD & Solaris confirmed). ... -
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MegEngine
Easy-to-use deep learning framework with 3 key features
MegEngine is a fast, scalable and easy-to-use deep learning framework with 3 key features. You can represent quantization/dynamic shape/image pre-processing and even derivation in one model. After training, just put everything into your model and inference it on any platform at ease. Speed and precision problems won't bother you anymore due to the same core inside. In training, GPU memory usage could go down to one-third at the cost of only one additional line, which enables the DTR...
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