On-Device Embedded Runtimes: Quantized Neural Execution on Edge Silicon

The Imperative of Embedded Execution

Modern foundation models and high-parameter vision transformers are frequently designed under the assumption of unlimited cloud datacenter GPU compute. In industrial automation, defense perception, and localized infrastructure, cloud dependencies introduce unacceptable vulnerabilities:

1. Latency volatility (jitter exceeding 150ms over cellular/broadband links).
2. Data exfiltration risks of proprietary manufacturing geometry and operational telemetry.
3. Total operational cessation during communications degradation or jamming.

Araskova Labs designs embedded neural runtimes and custom quantization kernels to execute advanced perceptual models directly on constrained silicon.

Technical Architecture

1. Post-Training Quantization (PTQ) & QAT Kernels

• Symmetric and asymmetric INT8 quantization schemes with layer-wise KL-divergence calibration, preserving >99.2% of FP32 baseline perceptual accuracy.
• Custom memory-aligned matrix-vector multiplication kernels exploiting ARM NEON SIMD instructions and NVIDIA Tensor Cores.

2. Zero-Copy IPC & Ring Buffers

• Shared POSIX memory ring buffers connecting camera hardware capture interfaces directly to neural compute graphs without intermediate CPU memory copies.
• Sustaining 120 FPS continuous multi-camera inference pipelines at under 15W total thermal design power (TDP).

3. Hard Real-Time Determinism (RTOS)

• Execution thread scheduling aligned with real-time Linux kernels (PREEMPT_RT) ensuring deterministic execution deadlines (<12ms jitter bounds).

Benchmarked Hardware Targets

• NVIDIA Jetson Orin Nano / AGX Orin: Full INT8 TensorRT deployment with dedicated DLA offloading.
• Industrial ARM Cortex-A72 / A53: Zero-dependency C++17 runtime with minimal static binary footprint (<8MB).
• Google Coral Edge TPU: Quantized TensorFlow Lite delegates with direct PCIe acceleration.

Published by Araskova Embedded Systems & Hardware Architecture Group.