AMEVA-Runtime

Next-Gen Unified On-Device Hardware Orchestration & 6-Modality AI Acceleration Runtime (with BitNet 1.58-bit Vulkan Compute) for Mobile & Edge

Architecture & Overview

AMEVA-Runtime is a hardware abstraction layer (HAL) and compute orchestration engine engineered specifically for mobile ARM64 devices (Android Termux, Linux Edge). It continuously inspects underlying silicon topology (/dev/kgsl-3d0, /dev/mali0) to route tensor execution across Qualcomm Adreno, ARM Mali, and ARM Cortex CPU-NEON backends.

6-Modality Acceleration Matrix

Empirical Physical Device Benchmarks

Tested on physical devices running Android 16 under Termux ARM64:

1. LLM Generation (Qwen2.5-0.5B-Instruct Q4_K_M)

2. Speech-to-Text (Whisper Large-v3-Turbo Q5_0, 548MB)

3. Text-to-Speech (AMEVA Bionic Native Vulkan Fleet Benchmarks)

4. BitNet 1.58-bit LLM (Microsoft BitNet b1.58 2B-4T i2_s)

Root-Cause Driver Solutions

1. ARM Mali-G68 Valhall Integer Truncation: Enforced medium tile matmul kernel dispatch (loadstride_b = 4 > 0), permanently eliminating shader zero-stride infinite loops on subgroup-16 hardware.
2. Qualcomm Adreno 830 JIT Register Bug: Bounded vector column specialization (mul_mat_vec_max_cols = 2), preventing compiler crash VK_ERROR_UNKNOWN (-13).
3. Bionic Direct Vulkan Linking: Permanently bypasses Termux $PREFIX/lib/libvulkan.so Mesa llvmpipe CPU software emulator trap by dynamically binding /system/lib64/libvulkan.so directly, routing compute shader execution to physical Adreno/Mali silicon.
4. Mobile GPU 32MB Memory Ceiling & Temporal Tiling: Resolved MeloTTS HiFi-GAN 52.4MB buffer overflow (VK_ERROR_OUT_OF_DEVICE_MEMORY / kernel TDR kill) via temporal chunk slicing ($T_{\text{chunk}} \le 819$) and Piper VITS on-chip SRAM tiling.
5. Zero-Silent-Fallback & Strict Model Resolution: Guaranteed fail-fast architecture without silent CPU degradation. Explicitly raises AmbiguousModelMatchError on multi-model ambiguity and rejects silent guessing.

Two-Track Supply Chain Architecture

To simultaneously guarantee 100% intellectual property & source code protection and frictionless, tokenless binary deployment, AMEVA enforces a strict Two-Track Supply Chain Architecture:

[Track 1: Local Ground Truth & Offline Staging]
├── dev/ameva/ameva-runtime/releases/ (Consolidated 6-Modality SOTA Binaries)
└── dev/termux/termux-*/releases/     (Domain-Specific Compiled Binaries)
                     │
                     ▼ Synchronized via Git & Automated Release Workflows
[Track 2: Remote Public GitHub Releases Cloud & CDN]
├── uno-km/ameva-runtime-releases    (Public Unified Binary Hub: Releases v2.7.3)
└── uno-km/termux-* (diffusion, stt) (Domain-Specific Public GitHub Releases)

1. Core Source Code (uno-km/ameva-runtime): Kept 100% Private. Architectural proprietary logic, HAL implementations, and optimization kernels are permanently protected.
2. Public Binary Distribution Hub (uno-km/ameva-runtime-releases): Public Registry. Hosts verified precompiled binaries, hardware acceleration compute shaders, and wheels for anonymous 1-Click installation without requiring personal GitHub tokens.

Installation & Engine Auto-Provisioning

1. Python SDK Installation

# Direct install from public release wheel
pip install --upgrade "https://github.com/uno-km/ameva-runtime-releases/releases/latest/download/ameva_runtime-2.7.3-py3-none-any.whl"

# Or standard PyPI install
pip install ameva-runtime

2. 1-Click Native Engine Provisioning

The NativeAssetManager automatically retrieves cryptographically verified native engine binaries from the public registry and establishes system links in $PREFIX/bin:

# Provision all hardware engines at once
ameva install --all

# Or provision specific engine modalities individually
ameva install --modality diffusion   # Stable Diffusion Vulkan Engine (sd-cli)
ameva install --modality stt         # Whisper STT Vulkan Engine (whisper-cli)
ameva install --modality llm         # LLaMA C++ Vulkan Engine (llama-cli)
ameva install --modality tts         # Sherpa-NCNN Neural Voice Engine
ameva install --modality bitnet      # BitNet 1.58-bit Acceleration Engine

# Validate system readiness with 12-stage hardware diagnostic
ameva doctor

Quickstart

Python SDK

import ameva_runtime as ameva
from ameva_runtime import vulkan

# 1. Inspect on-device silicon topology
profile = ameva.detect_hardware()
print(f"SoC: {profile.soc_name} | GPU: {profile.gpu_vendor}")

# 2. Run hardware self-test
doc = vulkan.Doctor()
report = doc.run_self_test()
print(f"GPU: {report.device_name} (Passed: {report.passed_stages}/{report.total_stages})")

Node.js / TypeScript

import { Doctor, createContext } from '@ameva/runtime';

const doc = new Doctor();
const report = await doc.runSelfTest();
console.log(`Vulkan GPU: ${report.deviceName}`);

Official Documentation & Benchmarks

• Official Architecture & API Reference
• Ecosystem Metrics & Registry Stats
• AMEVA Open-Source Foundation Portal

License

Licensed under the Apache-2.0 License. Copyright (c) 2026 Eunho Kim (@uno-km).