How to Select the right Edge AI Compute Solution: SIMCom SIM9650L, Espressif ESP32-P4, Advantech AOM-2721 & Nuvoton MA35D1

Understanding Edge AI Computing in Embedded Systems

For embedded system engineers, Edge AI computing refers to processing artificial intelligence workloads—such as computer vision, speech recognition, anomaly detection, or predictive analytics—directly on the device, without relying on continuous cloud connectivity.

This approach brings three major advantages compared with traditional embedded processing or cloud-based AI:

• Real-time decision-making – Local inference eliminates round-trip latency to cloud servers, enabling instant reactions in safety-critical or high-speed environments (e.g., industrial robotics, autonomous vehicles).

• Reduced data transfer and cost – Only processed results or event triggers need to be sent over networks, significantly lowering bandwidth requirements and operational costs.

• Improved privacy and resilience – Sensitive data can be analysed locally and discarded after processing, reducing exposure to interception and allowing systems to operate even with intermittent connectivity.

By integrating an AI-capable compute module into an embedded design, engineers can move from basic control and monitoring to autonomous, context-aware systems. Tasks that once required a dedicated server—like recognising product defects on a conveyor, optimising motor performance based on sensor fusion, or authenticating users via facial recognition—can now run entirely at the edge.

The transition to edge AI does require careful hardware selection. Processing requirements, thermal constraints, industrial interfaces, and security features all differ widely between module classes, making an informed choice essential.

Why Selecting the Right Edge AI Solution Matters

Not all edge AI compute solutions are created equal. Some excel at high-throughput multimedia processing, others are designed for secure, low-power control with AI acceleration, while others prioritise industrial connectivity or compatibility with existing OS and development stacks.

Selecting the right solution early in the design phase ensures:

• Adequate AI performance for the intended inference tasks

• Compatibility with required displays, sensors, and connectivity

• Thermal and power budget alignment for the deployment environment

• Long-term availability for production stability

This article compares four flagship options from different segments:

• SIMCom SIM9650 – Multimedia and IoT-focused AI smart module

• Espressif ESP32-P4 – Secure, low-power MCU-class AI controller

• Advantech AOM-2721 – High-performance embedded vision platform

• Nuvoton MA35D1 – Industrial Linux-capable HMI and AI SoC

What is TOPS - and what does it mean in practice?

TOPS stands for Tera Operations Per Second — essentially a measure of how many trillion operations a processor (often an AI accelerator or neural processing unit) can perform in one second.

In the context of edge AI compute solutions, TOPS is used to express AI inference performance, typically for operations like multiply–accumulate (MAC) used in neural networks.

A few points engineers should know about TOPS:

• It’s architecture-dependent – Different chips count “operations” differently, so a 14 TOPS rating on one module may not be directly comparable to another unless the test methodology is identical.

• It’s usually measured at INT8 precision – Many edge AI workloads are quantised to 8-bit integers for efficiency. Higher-precision (FP16, FP32) processing usually yields lower TOPS numbers.

• It’s a peak figure – Real-world performance can be lower due to memory bandwidth limits, model structure, or other system bottlenecks.

In short: TOPS is a useful headline metric for AI acceleration capability, but engineers should also look at actual benchmark results for their specific models before finalising a module choice.

Breaking down 14 TOPS

• 14 TOPS = 14 trillion operations per second

• For AI accelerators, an “operation” usually refers to a basic multiply–accumulate (MAC) used in neural network layers.

Example – Running an object detection model

Say you have a MobileNet-SSD type model for real-time object detection:

• It requires about 3 GMACs (3 billion MAC operations) per inference on a 300×300 image.

• If the module’s AI engine sustains 14 TOPS (14,000 GMACs per second at INT8):

14,000 GMAC/s ÷ 3 GMAC/inference = ~4,666 inferences/sec (theoretical max).

Reality check

• Real-world inferences per second will be lower — perhaps 25–50% of peak — due to memory bandwidth, pipeline stalls, and software overhead.

• Even at 25% efficiency, 14 TOPS could still deliver over 1,100 real-time inferences/sec for that model, far exceeding most edge needs.

• Engineers would typically use that surplus to:

  • Run multiple models in parallel

  • Process higher-resolution images

  • Increase model complexity for better accuracy

Why this matters

When comparing modules, TOPS tells you how much “AI headroom” you have.

• Low-TOPS modules (e.g., MCU-based with 0.1–0.5 TOPS) are great for keyword spotting or sensor anomaly detection.

• High-TOPS modules (10+ TOPS) open the door to multi-camera vision, real-time video analytics, or simultaneous AI workloads.

How to Select the Right Edge AI Compute Solution – An Engineer’s Step-by-Step Process

Selecting an edge AI compute solution is not about picking the most powerful device on paper — it’s about aligning the module’s capabilities with the specific functional, environmental, and lifecycle needs of your project. Below is a logical framework engineers can follow:

Step 1 – Define the AI Workload

• Model size and complexity: Will you run lightweight models (keyword spotting, anomaly detection) or heavy CNNs for vision?

• Performance requirement: Determine whether you need TOPS-heavy accelerators (e.g., SIMCom SIM9650) or a lower-power MCU-based approach (e.g., ESP32-P4).

• Inference vs. training: Edge modules typically handle inference only — but some SoCs can run lightweight on-device training if required.

Step 2 – Identify Sensor and Interface Needs

• Camera count and resolution: Multi-camera AI vision needs dedicated MIPI-CSI lanes and powerful ISP pipelines.

• Industrial I/O: For automation, ensure support for CAN-FD, UARTs, isolated GPIO, or fieldbus standards.

• Other sensors: Lidar, radar, microphones — check I²S, SPI, and high-speed interfaces are available.

Step 3 – Match the Connectivity Profile

• Local comms: Wi-Fi 6E, Bluetooth, or wired Ethernet for LAN-based processing.

• Wide-area comms: LTE/5G for remote AI nodes (SIMCom modules excel here).

• No connectivity? Prioritise modules optimised for full offline operation.

Step 4 – Evaluate OS and Software Ecosystem

• Application stack: Need Android for app development? Ubuntu for AI frameworks? Bare metal for deterministic control?

• Ecosystem maturity: Established SDKs, community support, and driver availability can reduce integration time.

Step 5 – Consider Power, Thermal, and Form Factor Constraints

• Power budget: Is it mains, PoE, battery, or energy harvesting?

• Thermal profile: Higher TOPS usually means higher thermal output — assess heatsinking and airflow requirements.

• Size: From tiny LGA MCUs to full OSM modules, ensure fit within enclosure and PCB footprint.

Step 6 – Validate Long-Term Availability and Reliability

• Production lifecycle: For industrial deployments, look for 7–10 years availability (Nuvoton, Advantech).

• Temperature rating: Ensure the module is qualified for the environment (-20°C to +70°C or more).

• Regulatory certifications: CE/FCC, RoHS, and application-specific standards (e.g., EN50155 for rail).

Step 7 – Prototype and Benchmark Early

Before committing to volume, test representative workloads on candidate modules to verify real-world inference speed, latency, and thermal stability.

SIMCom SIM9650 – AI Smart Module for Multimedia & IoT

• Processor: Octa-core ARM v8 up to 2.7GHz, Adreno 643 GPU

• AI performance: >14 TOPS via Hexagon Tensor Accelerator

• OS: Android 14

• Connectivity: LTE Cat 4, Wi-Fi 6E (2x2 MU-MIMO), Bluetooth 5.2, GNSS

• Memory: 4GB/8GB LPDDR4X + 64GB/128GB UFS

• Displays: Dual independent display (4K60 DP + FHD MIPI-DSI)

• Cameras: Up to 36MP multi-camera input with triple ISP

• I/O: PCIe Gen3, USB 3.1 Type-C, multiple UART/I2C/SPI/GPIO

• Applications: Smart POS, industrial handhelds, AI-enabled cameras, VR/AR, intelligent cockpits

Espressif ESP32-P4 – Secure MCU with AI Acceleration

• Processor: Dual-core RISC-V at up to 400MHz with AI instructions

• Security: Hardware cryptography, secure boot, trusted execution

• Memory: Integrated SRAM + external flash interface

• Connectivity: USB OTG, SDIO, Ethernet MAC, multiple SPI/I2C/UART

• AI role: Suitable for lightweight inference models, control logic with sensor fusion

• Low-power focus: Optimised for battery or energy-harvesting systems

• Applications: Secure IoT nodes, low-power AI gateways, portable AI devices

Advantech AOM-2721 – High-Performance Qualcomm QCS6490 Platform

• Processor: Cortex® Gold+ @ 2.7GHz + 3x Cortex® cores @ 2.4GHz

• Memory: Onboard 8GB LPDDR5 @ 8533MT/s

• GPU/VPU: Adreno GPU 643, VPU 633 (4K30 encode/decode)

• Displays: MIPI-DSI, eDP, DP outputs

• OS Support: Windows 11 IoT, Ubuntu, Yocto

• I/O: PCIe Gen3, USB 3.2, Ethernet, MIPI-CSI for cameras

• Form factor: OSM 1.1, 45 x 45mm

• Applications: Embedded vision, industrial AI gateways, high-resolution HMI systems

Nuvoton MA35D1 – Linux-Ready Industrial HMI and AI Control SoC

• Processor: Dual-core Cortex-A35 (Armv8-A)

• Memory: DDR interface for external RAM

• Security: Secure boot, TrustZone, hardware crypto

• I/O: CAN-FD, multiple UART, SPI, I2C, Ethernet

• AI role: Runs AI inference via external accelerators or optimised CPU instructions

• OS Support: Linux-based industrial applications

• Applications: Factory automation, transportation control, secure industrial gateways

Specification Comparison Table

Feature	SIMCom SIM9650	Espressif ESP32-P4	Advantech AOM-2721	Nuvoton MA35D1
CPU	Octa-core ARM v8, 2.7GHz	Dual-core RISC-V, 400MHz	Cortex Gold+ 2.7GHz + 3x2.4GHz	Dual-core Cortex-A35
AI Performance	>14 TOPS	Lightweight inference	GPU/VPU acceleration	CPU / external accelerator
Memory	4–8GB LPDDR4X + UFS	Integrated SRAM	8GB LPDDR5	External DDR
OS	Android 14	Bare metal/RTOS	Win 11 IoT, Ubuntu	Linux
Connectivity	LTE, Wi-Fi 6E, BT, GNSS	USB, Ethernet MAC	Ethernet, PCIe	Ethernet, CAN-FD
Displays	4K60 DP + FHD MIPI	Basic LCD via SPI/parallel	MIPI-DSI, eDP, DP	External controller
Camera	Up to 36MP multi-cam	External modules via SPI/I2C	Dual MIPI-CSI	External
Target Use	Multimedia IoT	Low-power control	High-end vision	Industrial HMI
Data Sheet	SIM9650L_series__SPEC_V1.03.pdf Download PDF • 641KB	esp32-p4_datasheet_en.pdf Download PDF • 1.58MB	AOM-2721_DS(061825)20250618154623.pdf Download PDF • 3.09MB	en-us--DS_MA35D1_Series_EN_Rev1.12.pdf Download PDF • 3.20MB

Application Guidance – Which Module Fits Which Project?

• High-end multimedia AI & connectivity: SIMCom SIM9650

• Low-power secure AI controllers: Espressif ESP32-P4

• Embedded vision & compute-intensive AI: Advantech AOM-2721

• Industrial HMI & control with AI hooks: Nuvoton MA35D1

Conclusion / Call to Action

Choosing the right edge AI compute solution starts with understanding the processing, connectivity, and application priorities of your project. Whether your priority is high-resolution multimedia AI, secure low-power control, or industrial Linux integration, one of these flagship options will align with your needs.

For full specifications, evaluation kits, and engineering samples, contact Ineltek’s technical team to discuss your edge AI requirements.

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FAQs - Choosing the right EDGE AI Compute module for your embedded system

Q. What is the main benefit of edge AI in embedded systems?

A. It enables real-time AI processing locally, reducing latency, bandwidth costs, and privacy risks.

Q. Which module is best for multi-camera AI vision?

A. The SIMCom SIM9650 and Advantech AOM-2721 are strongest for high-resolution camera input and processing.

Q. Which option suits harsh industrial environments?

A. Nuvoton MA35D1 offers industrial interfaces and long-term Linux support.

Q. Can low-power devices still run AI models effectively?

A. Yes, with optimised lightweight models, the ESP32-P4 can run local inference on constrained power budgets.

Q. How do I estimate the AI performance I actually need?

A. Profile your model on a desktop environment, then scale to the module’s architecture. If the module runs your model with at least 30% performance headroom, it’s a safe choice.

Q. Can I run multiple AI workloads in parallel?

A. Yes, if the module supports multi-core processing and adequate memory bandwidth — the Advantech AOM-2721 and SIMCom SIM9650 are well-suited for concurrent inference and application logic.

Q. What if my AI model changes during product life?

A. Choose a module with firmware/OTA upgrade support and enough processing headroom to handle heavier models without redesign.

Q. Is there a trade-off between AI power and battery life?

A. Yes — higher TOPS modules consume more power. For portable devices, balance model complexity with available energy budget.

Q. How important is hardware security in edge AI?

A. For systems handling sensitive data, features like secure boot, encryption engines, and trusted execution environments (as in Nuvoton MA35D1 and ESP32-P4) are critical to prevent tampering and protect inference data.