Gettobyte Edge AI Engennering Service Tooling and Platform

We Engineer Edge AI Across Every Platform.

From automotive-grade microcontrollers to high-performance AI accelerators — Gettobyte’s engineers work across leading Edge AI hardware and software ecosystems, optimizing your models for real-time, reliable deployment.
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Introduction – The Multi-Vendor
Edge AI Landscape

Edge AI is not a single technology — it’s a combination of chipsets, software stacks, and toolchains from multiple global
vendors.
Each ecosystem comes with its own strengths — from ultra-low-power MCUs to multi-core MPUs with NPUs and GPUs.

At Gettobyte, we help you choose, integrate, and optimize the right platform for your product — whether it’s a smart
meter, autonomous vehicle controller, or industrial node.

For Visually Understand

Edge AI Hardware Ecosystem
(Chips & Boards)

We Build on the Best AI-Enabled Processors and SoCs

Vendor

Core Hardware

Edge AI Capabilities

Gettobyte Expertise

NXP Semiconductors

S32K3, S32G, i.MX93

Heterogeneous MCUs/MPUs with NPUs, HSE security

Automotive-grade MCAL, eIQ integration, HSM-secure ML

STMicroelectronics

STM32H7, STM32MP2

MCU + MIPI + DSP acceleration, STM32Cube.AI

Edge model deployment with STM32Cube.AI, MDMA optimizations

Texas Instruments

Sitara AM62A, Jacinto TDA4VM

On-chip DSP + ML accelerator cores

TIDL flow, DSP pipeline optimization for ADAS & industrial AI

Infineon

PSoC™ 6, Traveo II, AURIX

Safety-certified automotive MCUs with AI peripheral
support

Secure edge AI deployment, power efficient RT inference

NVIDIA Jetson

Jetson Orin Nano / Xavier / TX2

High-end GPU/NPU edge AI modules

Vision inference, TensorRT acceleration, real-time pipeline tuning

Qualcomm / Snapdragon

QCS6490 / RB5 / SA8155P

Integrated NPU, AI Engine SDK

AI perception pipelines for robotics and automotive dashboards

Renesas

RZ/V2L, R-Car H3

DRP-AI accelerators

BSP bring-up, DRP-AI model compiler integration

Espressif Systems

ESP32-S3 / ESP32 C6

Low-power MCU with vector extensions

TinyML deployments using TFLite Micro & Edge Impulse

Sony / Ambarella / Hailo /
SiMa.ai / Axelera.ai

Dedicated Edge AI chips

Specialized NPUs for industrial & automotive edge

Gettobyte supports integration and software adaptation on partner kits

Edge AI Software Ecosystem
(Tools & Frameworks)

 From Training to Deployment — We Master the Edge AI Stack

Category

Vendor/Framework

Description

Gettobyte Capability

AI Compilers & SDKs

NXP eIQ, TI TIDL, STM32Cube.AI, NVIDIA TensorRT, OpenVINO, ONNX Runtime

Convert and optimize models for specific chipsets

Quantization, pruning,
hardware-aware
optimization

Inference Engines

TensorFlow Lite Micro, Glow, CMSIS NN, SNPE, PyTorch Mobile, DeepX SDK

Real-time model inference on MCUs/MPUs

Runtime integration, latency
tuning, memory
optimization

Model Training & Development Tools

Edge Impulse, eIQ Toolkit, Google Colab, PyTorch, TensorFlow

Train and test models using sensor data

Dataset prep, model
training, Edge-target
calibration

Sensor Data Tools & DSP Libraries

CMSIS-DSP, Eigen, Librosa, MATLAB Embedded Coder

Feature extraction and
signal preprocessing

Custom C/C++ feature
modules, fixed-point
validation

Deployment Pipelines

Yocto Linux, FreeRTOS, Zephyr RTOS

Edge OS and middleware
integration

Build pipelines, OTA, and
containerized deployments

Security Frameworks

MbedTLS, wolfSSL, PKCS#11

Secure model storage and
inference

Key management, secure
boot integration

Why Multi-Platform Capability Matters

Every product has different design goals — cost, performance, safety, or certification.
That’s why no single Edge AI platform fits all.

Gettobyte engineers analyze your latency, power, and memory constraints to help you select

The right SoC or MCU for your use case

The best AI runtime framework

The most efficient model optimization pipeline

We’ve already worked with NXP, STM32, ESP32, and NVIDIA Jetson platforms for projects in automotive ECUs, smart meters, and industrial automation systems.

How We Help You Leverage These
Platforms

 From Board Bring-Up to On-Device Inference

Hardware Bring-Up & BSP Integration
→ Setting up peripheral drivers, DMA, and sensor interfaces.
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AI Model Conversion & Optimization
→ Deploying quantized models with eIQ, TIDL, or TensorRT.
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Firmware Integration & Scheduling
→ RTOS task design for AI pipelines (CAN, ADC, Ethernet).
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Security Enablement
→ Secure boot, key provisioning, and model encryption.
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Testing & Validation
→ Real-time latency, power, and accuracy profiling on hardware.
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Trusted by OEMs and Innovators

Our cross-platform expertise helps automotive, energy, and industrial clients build scalable, hardware-agnostic AI
systems — ensuring long-term flexibility and vendor independence.

Gettobyte

Choose the Right Edge AI Platform for Your Product.

We’ll help you pick, optimize, and deploy your AI on the best-suited hardware and software combination.

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