SiMa.ai Hardware Documentation

Use this site for hardware setup, firmware workflows, and low-level interfaces for SiMa.ai MLSoC Modalix products. Software, ML, and pipeline tooling are documented separately.

About the MLSoC Modalix

The MLSoC Modalix is SiMa.ai's second-generation machine-learning system-on-chip. It combines an Arm Cortex-A65 application complex, a high-throughput Machine Learning Accelerator (MLA), an Image Signal Processor (ISP) for camera ingest, and a Computer Vision Unit (CVU) for classical vision workloads — all on a single die.

This integration lets you run multimodal, generative, and vision AI pipelines at the edge without combining separate accelerators, host CPUs, and camera bridges. The Modalix SoM packages MLSoC Modalix for carrier-board and product integration, and every DevKit documented here uses the same silicon.

DevKit Portfolio

SiMa.ai offers MLSoC Modalix-based development kits and PCIe products for edge AI applications. Select a product below, or compare features in the table.

Modalix DevKit

The current development kit for evaluating MLSoC Modalix and building applications on the Modalix SoM.

Modalix PCIe Card

Half-Height/Half-Length (HHHL) card designed for extensible edge compute systems.

Modalix Early Access DevKit

Legacy development kit for existing Early Access customers.

Feature Comparison

Feature	Modalix DevKit	Modalix PCIe Card	Modalix Early Access DevKit
Documentation & OS
Product Brief	view	—	view
Preloaded Operating System	eLxr Linux	eLxr Linux	eLxr Linux
Compute
ARM Cores	8x Arm Cortex-A65 @ 1.4 GHz	8x Arm Cortex-A65 @ 1.4 GHz	8x Arm Cortex-A65 @ 1.4 GHz
ISP (Image Signal Processor)	Arm C-71 @ 1.2 GHz	Arm C-71 @ 1.2 GHz	Arm C-71 @ 1.2 GHz
CVU (Computer Vision Unit)	Synopsys EV74 @ 750 16-bit GOPS	Synopsys EV74 @ 750 16-bit GOPS	Synopsys EV74 @ 750 16-bit GOPS
Memory & Storage
RAM Size	32 GB LPDDR5	32 GB LPDDR5	64 GB LPDDR5
Storage	16 GB eMMC, 500 GB NVMe	16 GB eMMC	10 GB eMMC
SD Card Slot	✖	✔	✔
Networking
Ethernet	1x 1GbE	1x 1GbE	1x 1GbE (end0), 1x 10GbE (end1), 2x 10GbE SFP+ (end2/3)
Camera Inputs
MIPI CSI	2x 2-lane MIPI CSI	✖	4x 4-lane MIPI CSI
GMSL2 over FAKRA	✖	2x GMSL2 over FAKRA	✖
I/O & Display
GPIO / Headers	40-pin GPIO header	✖	40-pin GPIO header
USB	4 USB 3.0 ports	✖	✖
HDMI	1 HDMI 1.4 port	✖	✖
Graphics Controller	Silicon Motion SM768	✖	✖
Video Codecs
H.264/H.265 Encoder	4kp60	4kp60	4kp60
MJPEG Encoder	4kp30	4kp30	4kp30
H.264/H.265 Decoder	4kp60	4kp60	4kp60
AV1 and MJPEG Decoder	4kp60	4kp60	4kp60
Form Factor
Host-attached PCIe card	✖	✔	✖

Order Your DevKit

Deployment Architectures

MLSoC Modalix supports two common development architectures: standalone mode and PCIe mode. Choose the architecture that matches how your hardware is connected and where your application runs.

Standalone Mode

In standalone mode, a Modalix DevKit or a custom Modalix SoM-based system runs as a self-contained device. Use this mode when the device receives data directly from sensors, cameras, storage, or network interfaces.

Key Use Cases

• Edge AI applications: Run inference without relying on a central server or cloud infrastructure.
• Compact deployments: Reduce the need for additional host hardware.
• Power-constrained systems: Support remote monitoring systems powered by batteries or solar panels.

Advantages

• Self-contained: Runs without a host machine.
• Power efficient: Supports power-sensitive environments.
• Compact: Fits space-constrained deployments.

Typical Data Flow

1. The DevKit receives data directly from network interfaces or sensors.
2. The DevKit loads a NEAT application that defines the on-device inference pipeline.
3. MLSoC Modalix performs inferencing and processes the data locally.
4. Results are sent to other devices or systems via network connections for further action or visualization.

Set up standalone mode

PCIe Mode

In PCIe mode, the Modalix PCIe Card is installed in a host machine. Use this mode when a server, desktop, or workstation handles orchestration, storage, and I/O while the card provides Modalix inference acceleration.

Key Use Cases

• High-performance systems: Use a data-center server or workstation for data processing and storage while the Modalix PCIe Card runs inference.
• Host-managed I/O: Use host storage, networking, or sensor ingest while offloading inference to the card.

Advantages

• Dedicated acceleration: Offload inference from the host CPU to Modalix hardware.
• Expandable capacity: Add inference cards as demand grows.

Typical Data Processing Flow

1. The host machine captures data from sensors, peripherals, storage, or network interfaces.
2. The Modalix PCIe Card loads a NEAT application that packages the inference pipeline and adapted AI model.
3. The host sends data to the Modalix PCIe Card over PCIe.
4. The host processes the results or forwards them to downstream systems.

Set up PCIe mode

Get Started

Pick the path that matches your setup. Each guide covers serial-console access, network bring-up, and firmware management for that mode.

Quick Start Guide

Step-by-step interactive walkthrough for first-time DevKit bring-up.

Standalone Mode

Run a Modalix DevKit or Modalix SoM-based system as a self-contained edge device.

PCIe Mode

Install the Modalix PCIe Card into a host machine for offloaded inference.