AMEVA-Runtime: Unified On-Device Hardware Orchestration & Multi-Modal AI Acceleration

AMEVA-Runtime is an enterprise-grade hardware abstraction layer (HAL) and compute orchestration engine engineered specifically for mobile ARM64 environments (Android Termux, Linux Edge). It continuously inspects underlying silicon topology (/dev/kgsl-3d0, /dev/mali0) to dynamically route tensor workloads across Qualcomm Adreno, ARM Mali, and ARM Cortex CPU-NEON backends. By enforcing a strict Zero-Silent-Fallback Protocol and resolving vendor-specific GPU driver compiler bugs, AMEVA-Runtime delivers up to 35.8x acceleration over baseline CPU execution with zero thermal runaway.

1. Installation Guide

AMEVA-Runtime is distributed across Python (PyPI) and Node.js (npm). It operates entirely within unprivileged user-space on Android Termux (ARM64/AArch64) and Linux edge environments without requiring root privileges.

1.1 Prerequisites on Android Termux

Update package repositories and install foundational build and runtime dependencies:

pkg update -y
pkg install -y clang python python-numpy nodejs termux-api git curl

1.2 Python SDK & Global CLI Installation

Install the core orchestration engine via pip:

pip install --upgrade pip
pip install ameva-runtime

To install with full multi-modal engine integrations (STT, TTS, LLM, Diffusion, Vision, BitNet):

pip install "ameva-runtime[all]"

1.3 1-Click Native Hardware Asset Provisioning & Dual-Track Compatibility

Rather than requiring users to manually compile C/C++ source trees, setup CMake/Clang toolchains, or configure OpenMP and Vulkan loaders, AMEVA-Runtime includes an automated, atomic hardware provisioner (NativeAssetManager):

# 1-Click Auto-Provision all 6 modalities and native hardware drivers
ameva install --all

# Or alias
ameva setup

# Force overwrite existing binaries with fresh GitHub Release assets
ameva install --all --force

# Provision a single targeted modality
ameva install --modality diffusion --force

Dual-Track Compatibility Architecture (Track A + Track B)

AMEVA-Runtime enforces a Dual-Track Deployment Architecture to ensure zero-regression interoperability between modern unified environments and existing ecosystem toolchains:

Programmatic Python Provisioning API

You can also trigger atomic asset provisioning directly within Python workflows:

from ameva_runtime import provision_native_assets

# Provision all native binaries and link compatibility bridges
results = provision_native_assets(force=False)
print("Provisioning Status:", results)

1.4 Node.js / TypeScript SDK & CLI Installation

Install globally or as a project dependency via npm:

# Global CLI tools (ameva, ameva-run, ameva-gpu)
npm install -g @ameva/runtime

# Local project dependency
npm install @ameva/runtime

1.5 Android Bionic Vulkan Dynamic ICD Discovery

AMEVA-Runtime communicates directly with the vendor Vulkan Installable Client Driver (ICD) provided by the Android OS:

• Primary Search Path: /system/lib64/libvulkan.so (Bionic C ABI)
• Secondary Search Path: /vendor/lib64/libvulkan.so
• Zero Termux-Mesa Conflict: AMEVA-Runtime automatically bypasses unaccelerated software Mesa loaders ($PREFIX/lib/libvulkan.so) in favor of direct hardware Bionic ICD binding.

2. Basic Usage Guide

AMEVA-Runtime provides unified diagnostics, hardware topology profiling, and inference orchestration across CLI, Python, and Node.js.

2.1 Command-Line Interface (CLI)

# 1. Execute 12-Stage Diagnostic Doctor Self-Test
ameva doctor

# 2. Inspect SoC, GPU Topology, and CPU Cgroup Affinity
ameva profile

# 3. Dry-Run SmartRouter Execution Plan for a Model
ameva plan -m qwen2.5-0.5b-instruct.gguf --backend vulkan

# 4. Safely Execute Model Inference with Optimal Hardware Offload
ameva exec -m qwen2.5-0.5b-instruct.gguf -p "Explain quantum computing in 2 sentences."

# 5. Inspect Multi-Modal Adapters and Run Micro-GEMM Benchmark
ameva benchmark

2.2 Python SDK Quickstart

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_model} | GPU: {profile.gpu_family} (Driver: {profile.driver_version})")
print(f"Recommended Backend: {profile.recommended_backend} | Threads: {profile.recommended_threads}")

# 2. Execute 12-stage hardware diagnostic
doc = vulkan.Doctor()
report = doc.run_self_test(verbose=False)
print(f"Diagnostic Passed: {report.passed_stages}/{report.total_stages} stages (Success: {report.overall_success})")

2.3 Node.js / TypeScript SDK Quickstart

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

async function main() {
  // 1. Quick probe for Vulkan compute availability
  if (!isAvailable()) {
    console.warn('Vulkan GPU acceleration unavailable; falling back to CPU NEON.');
    return;
  }

  // 2. Run diagnostic self-test
  const doc = new Doctor();
  const report = await doc.runSelfTest();
  console.log(`GPU Device: ${report.deviceName} | Vendor ID: ${report.vendorId}`);
  console.log(`Vulkan Stages: ${report.passedStages}/${report.totalStages} passed in ${report.totalElapsedMs}ms`);
}

main().catch(console.error);

3. Advanced Production Architecture

AMEVA-Runtime acts as the central nerve center for mobile on-device AI, orchestrating a 6-Modality execution mesh.

                  +-------------------------------------------------------+
                  |                     AMEVA-Runtime                     |
                  |            Unified Hardware Orchestration             |
                  +---------------------------+---------------------------+
                                              |
                     +------------------------+------------------------+
                     |                                                 |
         +-----------v-----------+                         +-----------v-----------+
         |    SmartRouter        |                         |     Doctor Engine     |
         |  Silicon & Cgroup HAL |                         |   12-Stage Diagnostic |
         +-----------+-----------+                         +-----------+-----------+
                     |                                                 |
  +------------------+-------------------------------------------------+------------------+
  |                  |                  |                  |                  |           |
+-v--------+   +-----v----+       +-----v----+       +-----v----+       +-----v----+  +---v------+
|   STT    |   |   TTS    |       |   LLM    |       |Diffusion |       |  Vision  |  |  BitNet  |
| Whisper  |   |  Piper   |       | LlamaCpp |       |   SDXS   |       |   ViT    |  |  1-Bit   |
+----------+   +----------+       +----------+       +----------+       +----------+  +----------+

3.1 Multi-Modal Adapter Bindings

Downstream engines dynamically bind to AMEVA-Runtime through standardized adapter protocols:

from ameva_runtime.adapters import (
    SttAdapter,
    TtsAdapter,
    LlamaCppAdapter,
    DiffusionAdapter,
    VisionAdapter,
    BitnetAdapter,
)
from ameva_runtime import get_runtime

runtime = get_runtime()
profile = runtime.profile

# Bind multi-modal engines to optimal silicon backends
stt_binding = SttAdapter.bind(engine_instance=None, diagnostic_report=profile)
tts_binding = TtsAdapter.bind(engine_instance=None, diagnostic_report=profile)
llm_binding = LlamaCppAdapter.bind(engine_instance=None, diagnostic_report=profile)

print(f"STT Backend : {stt_binding.backend} (GPU: {stt_binding.is_vulkan})")
print(f"TTS Backend : {tts_binding.backend} (Shader: {tts_binding.config.get('shader_type')})")
print(f"LLM Backend : {llm_binding.backend} (VRAM Layers: {llm_binding.config.get('ngl')})")

3.2 Custom Vulkan Context & Memory Pooling

For latency-critical multi-tenant inference, manage native VkDevice handles and host-coherent staging memory pools directly:

from ameva_runtime import vulkan as avr

# Create isolated Vulkan compute context
ctx = avr.get_or_create_context(device_id="gpu:0")

# Query physical device memory topology
mem_props = ctx.get_memory_properties()
print(f"Device Local Heap: {mem_props['device_local_mb']} MB")
print(f"Host Visible Heap: {mem_props['host_visible_mb']} MB")

4. Feature Breakdown & Parameter Specification

4.1 12-Stage Diagnostic Suite (vulkan.Doctor)

The Doctor engine enforces absolute binary integrity by dispatching actual Vulkan C ABI calls (ctypes) with RAII handle destruction:

4.2 SmartRouter Execution Plan Parameters

4.3 Zero-Silent-Fallback Guarantee

AMEVA-Runtime rejects silent fallback to CPU when GPU acceleration is explicitly commanded:

• [ERROR: AMEVA-RUNTIME-E001]: Vulkan was requested but libvulkan.so Bionic loader cannot be opened.
• [ERROR: AMEVA-RUNTIME-E002]: Driver initialization failed or GPU device does not support compute queues.
• Rationale: Silent fallback causes unexpected 100% CPU thread starvation, rapid thermal runaway (up to 45°C+), and battery drain on mobile silicon.

5. Real-World Production Examples

5.1 End-to-End Multi-Modal Pipeline (Speech-to-Text -> LLM -> Text-to-Speech)

import termux_stt
import termux_tts
from ameva_runtime import get_runtime

# Initialize runtime
runtime = get_runtime()
profile = runtime.profile
print(f"Active Hardware: {profile.soc_model} ({profile.gpu_family})")

# 1. Transcribe speech input (Whisper GPU / CPU-NEON)
stt = termux_stt.create_engine("whisper", model="base", device="auto")
transcript = stt.transcribe("query.wav")
print(f"User Query: {transcript.text}")

# 2. Generate LLM response via SmartRouter
llm_result = runtime.execute(
    model_path="qwen2.5-0.5b-instruct.gguf",
    prompt=f"<|im_start|>user\n{transcript.text}<|im_end|>\n<|im_start|>assistant\n",
    max_tokens=64,
)
print(f"LLM Output: {llm_result.text}")

# 3. Synthesize vocal response (Piper Vulkan / NCNN)
tts = termux_tts.create_engine("piper", model="lessac-medium", device="auto")
tts.synthesize(llm_result.text, output_file="response.wav")
print("Response generated: response.wav")

5.2 TypeScript High-Availability Hardware Monitor

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

async function monitorHardware() {
  const doc = new Doctor();
  const report = await doc.runSelfTest();

  if (!report.overallSuccess) {
    console.error(`[ALERT] Hardware integrity compromised: ${report.diagnosisReason}`);
    process.exit(1);
  }

  console.log(`[STATUS] Hardware Verified: ${report.deviceName}`);
  console.log(`[STATUS] Active Driver: ${report.driverVersion}`);
}

setInterval(monitorHardware, 60000);

6. Concrete Execution Output Artifacts

6.1 ameva doctor Output Artifact

==============================================================
  AMEVA-Vulkan-Runtime: 12-Stage Diagnostic Suite (V0-V11)
==============================================================
  [V0] Vulkan Loader Open            : PASS (0.42 ms) -> /system/lib64/libvulkan.so
  [V1] Instance Creation             : PASS (1.18 ms) -> ApiVersion: 1.3.280
  [V2] Physical Device Enumeration   : PASS (0.85 ms) -> Found 1 device(s)
  [V3] Hardware GPU Selection        : PASS (0.31 ms) -> Adreno (TM) 830
  [V4] Compute Queue Family Probe    : PASS (0.22 ms) -> Queue Family #0 (Flags: 0x000E)
  [V5] Logical Device Creation       : PASS (2.64 ms) -> Features enabled: 16-bit, subgroups
  [V6] Buffer Memory Allocation      : PASS (0.94 ms) -> 64 KB allocated (Host-Coherent)
  [V7] SPIR-V Pipeline Compilation   : PASS (3.11 ms) -> Compute pipeline bound
  [V8] Compute Shader Dispatch       : PASS (0.88 ms) -> 64 workgroups dispatched
  [V9] Result Checksum Validation    : PASS (0.15 ms) -> Expected: 0x5F12, Got: 0x5F12
  [V10] GGML MatMul Tensor Ops       : PASS (4.25 ms) -> GEMM 256x256 Float32 verified
  [V11] End-to-End Model Inference   : PASS (6.10 ms) -> Multi-modal pipe ready
--------------------------------------------------------------
[RESULT] Passed 12/12 stages in 21.05 ms. Status: STABLE.

6.2 ameva profile Output Artifact

=================================================================
  AMEVA Runtime: Hardware & System Topology Profile
=================================================================
  Vendor / Architecture : Qualcomm Technologies, Inc. (ARM64-v8a)
  SoC Model             : Snapdragon 8 Elite (SM8750)
  GPU Family / Driver   : Qualcomm Adreno 830 (Driver: 512.782.0)
  Vulkan Loader Available: YES (/system/lib64/libvulkan.so)
  OpenCL Available      : YES (/system/vendor/lib64/libOpenCL.so)
  NPU Available         : YES (Hexagon v79 HTP)
-----------------------------------------------------------------
  CPU Online Cores      : 8 (2x Prime 4.32GHz, 6x Performance 3.53GHz)
  CPU Allowed Cores     : [0, 1, 2, 3, 4, 5, 6, 7]
  Cgroup Restrained     : NO
  Memory Available      : 15480 MB total (9820 MB free)
-----------------------------------------------------------------
  Recommended Backend   : VULKAN
  Optimal Thread Count  : 6
=================================================================

7. Mobile GPU Interconnect Architecture

AMEVA-Runtime bypasses intermediate user-space emulation layers by binding directly to the Android Bionic C runtime ABI.

+-------------------------------------------------------------+
|                 Termux Unprivileged User-Space              |
|                                                             |
|   +-----------------------------------------------------+   |
|   |         AMEVA-Runtime (Python / Node.js)            |   |
|   +--------------------------+--------------------------+   |
+------------------------------|------------------------------+
                               | Direct dlopen()
+------------------------------v------------------------------+
|                     Android Bionic C ABI                    |
|             Path: /system/lib64/libvulkan.so                |
+------------------------------+------------------------------+
                               | Direct Kernel ioctl()
+------------------------------v------------------------------+
|                   Android Kernel DRM Nodes                  |
|        Qualcomm: /dev/kgsl-3d0   |   ARM Mali: /dev/mali0   |
+------------------------------+------------------------------+
                               | Direct Hardware Execution
+------------------------------v------------------------------+
|                   Physical Mobile Silicon                   |
|     Qualcomm Adreno 830 / 740    |    ARM Mali-G68 / G715   |
+-------------------------------------------------------------+

7.1 Vendor Compatibility Matrix

8-1. In-Depth Comparative Analysis: CPU vs. GPU Acceleration

Empirical benchmarks collected on physical Android devices under sustained multi-modal execution:

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

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

3. Text-to-Speech (Termux-TTS v1.3.0 Vulkan)

4. Thermal Dissipation & Power Efficiency Profiles

• Power Consumption per Token:
  • CPU-NEON (8 cores pegged at 100%): 4.8W – 6.2W average battery draw.
  • Vulkan GPU Offload (Adreno 830 compute queue): 1.8W – 2.4W average battery draw (~58% energy reduction).
• Thermal Throttling Horizon (Continuous 30-Minute Run):
  • CPU-NEON: Device skin temperature exceeds 44°C within 7 minutes; CPU core frequencies throttle down by 45%.
  • Vulkan GPU: Device skin temperature stabilizes at 37°C–39°C due to unified memory compute efficiency; zero thermal throttling triggered.

9. Hardware Prerequisites & Engineering Constraints

9.1 Minimum vs. Recommended Specifications

9.2 Known Technical Boundaries

• Virtualization Overhead: Termux PRoot/chroot environments introduce memory copy penalties. AMEVA-Runtime is optimized for native Termux user-space.
• 32-Bit Deprecation: Pure 64-bit (arm64-v8a / aarch64) architecture is strictly enforced; 32-bit armeabi-v7a binaries are rejected.
• Display Swapchains: In headless server environments, Vulkan surface presentation (VK_KHR_surface) is intentionally omitted; compute queues operate strictly headless.

10. 24/7 Uninterrupted Background Execution Guide

To maintain continuous 24/7 autonomous inference without OS process termination, configure the 3-tier mobile stability pipeline:

Step 1: Termux Background Lock

Prevent the Android kernel from freezing CPU cycles when the display turns off:

termux-wake-lock

Step 2: Android OS Battery Optimization Exemption

1. Navigate to Android Settings -> Apps -> Termux.
2. Select Battery -> Change policy to Unrestricted (prevents background CPU throttling by Samsung Device Care / MIUI PowerKeeper).
3. If using Samsung One UI: Exclude Termux from Sleeping apps and Deep sleeping apps.

Step 3: Android 12+ Phantom Process Killer Deactivation

Android 12 Introduced a strict limit (32 child processes) that terminates high-performance background daemons. Disable this limit permanently via ADB:

# Connect device to PC via USB and enable USB Debugging
adb devices

# 1. Disable Phantom Process Limiter
adb shell "/system/bin/device_config put activity_manager max_phantom_processes 2147483647"

# 2. Prevent automated cloud sync override across reboots
adb shell "/system/bin/device_config set_sync_disabled_for_tests persistent"

# 3. Verify configuration
adb shell "/system/bin/device_config get activity_manager max_phantom_processes"
# Expected output: 2147483647

11. Enterprise Licensing & Open-Source Compliance

AMEVA-Runtime is published under the Apache License, Version 2.0.

• Permissive Commercial Use: Commercial deployment, modification, sublicensing, and private distribution are fully permitted.
• Patent Grant: Explicit contributor patent grant protects downstream integrators against patent infringement claims.
• Non-Viral Architecture: Permissive Apache-2.0 licensing ensures upstream integration without forcing downstream applications to open-source proprietary codebases.
• Copyright: Copyright (c) 2026 Eunho Kim (@uno-km) & AMEVA Open-Source Foundation.

12. Strategic Technical Keywords

vulkan-compute, mobile-gpu, hardware-acceleration, hardware-abstraction-layer, adreno-gpu, arm-mali, snapdragon-8-elite, exynos, termux, on-device-ai, edge-ai, tensor-acceleration, spir-v, compute-shaders, zero-silent-fallback, llamacpp, whisper-cpp, sherpa-onnx, stable-diffusion, vision-language-models, bitnet, gguf, ncnn, bionic-loader, arm64, aarch64, cgroup-management, cpu-neon, thermal-throttling, power-efficiency, smart-router, hardware-orchestration, multi-modal-ai, subgroup-operations, gemm-acceleration, mobile-vlm, speech-to-text, text-to-speech, image-generation, edge-inference, unprivileged-userspace, termux-wake-lock, phantom-process-killer, android-ai-runtime, valhall-gpu, adreno-830, mali-g68, ameva-foundation, uno-km, open-source-ai