Edge AI systems are becoming more complex, more power constrained, and increasingly safety-critical. From ADAS domain controllers and industrial robotics to smart cameras and medical imaging platforms, modern embedded systems must process vast amounts of sensor data, run AI inference models, and execute real-time control decisions all within tight latency and power budgets.
Traditionally, achieving these required multiple devices: an FPGA for sensor preprocessing, a discrete AI accelerator for inference, and a CPU or GPU for postprocessing and system control. This fragmented architecture increases latency, power consumption, board complexity, and design risk.
A new architectural shift is emerging single-chip intelligence, where preprocessing, AI inference, and postprocessing are consolidated into one adaptive device. At the forefront of this shift is the Versal AI Edge Series Gen2 FPGAs from AMD.
The three phases of Edge AI Compute
To understand the significance of this shift, it is important to examine the three core phases of compute in modern edge AI Systems.
Pre-processing – Sensor conditioning and Data Preparation
Edge systems ingest raw data from cameras, LiDAR, radar, industrial sensors, or medical imaging sources. This data must be:
- Filtered and normalized
- Channelized or fused
- Formatted for AI pipelines
- Processed with deterministic latency
Preprocessing is typically highly parallel and latency-sensitive. Programmable logic is ideal for implementing custom pipelines that operate deterministically without cache-related jitter.
AI Inference – Neural Network Execution
Once data is conditioned, it is passed to neural network models for object detection, classification, segmentation, anomaly detection, or decision-making.
AI inference workloads demand:
- High MAC throughput
- Efficient memory bandwidth
- Support for modern data types (FP16, INT8, emerging formats)
- Strong performance per watt
Dedicated AI engines are optimized for these workloads but must be tightly coupled to memory and preprocessing pipelines to avoid bottlenecks.
Postprocessing – Decision, Control, and Feedback
After inference, systems must:
- Apply decision logic
- Run control algorithms
- Interface with actuators
- Manage communication stacks
- Handle safety monitoring
This phase requires robust scalar compute, real-time determinism, and often functional safety compliance.
From Multi-Chip Complexity to Single-Chip Intelligence
Edge AI systems have traditionally relied on distributed compute across multiple devices. The architectural trade-offs become clear when comparing conventional multi-chip implementations with a consolidated adaptive SoC approach powered by the Versal AI Edge Series Gen2 FPGA.
| The Limitations of Multi-Chip Architectures | Enabling True Single-Chip Intelligence with Versal AI Edge Gen2 |
|---|---|
| Separate FPGA for preprocessing, AI accelerator for inference, and CPU/GPU for control | Preprocessing, inference, and postprocessing integrated into one adaptive SoC |
| Multiple memory domains and frequent data transfers between chips | Unified memory access through integrated NoC and LPDDR5X support |
| Inter-device communication adds latency | Deterministic on-chip data movement between PL, AIE-ML v2, and CPUs |
| Higher power consumption due to multiple devices | Improved performance-per-watt through heterogeneous integration |
| Complex PCB routing and signal integrity challenges | Reduced board complexity and smaller footprint |
| Increased synchronization and clock domain management | Tight internal clocking and architectural cohesion |
| Longer validation and safety certification cycles | Consolidated safety architecture (ASIL D / SIL 3 capable designs) |
| Higher BOM cost and integration effort | Device consolidation reduces system complexity and risk |
Deterministic Preprocessing in Programmable Logic
The programmable logic fabric supports custom hardware pipelines for sensor fusion, image signal processing, data conditioning, low-latency filtering, and deterministic data formatting. Because preprocessing remains within the same device as inference, latency is minimized and data movement overhead is reduced.
Efficient AI Inference with AIE-ML v2
The AI Engine-ML v2 architecture in Gen2 devices is optimized for high compute density and improved TOPS-per-watt efficiency compared to previous generations. With support for modern numerical formats and enhanced memory bandwidth, inference workloads can execute closer to the sensor pipeline without requiring discrete accelerators. This reduces board complexity while improving performance consistency.
High-Performance Scalar Compute for Postprocessing
The significant increase in scalar compute capability including Cortex-A78AE cores enables complex decision logic, control algorithms, and higher-level system software to execute on the same device. For safety-critical applications such as ADAS and industrial robotics, lockstep processing modes, and real-time cores support ASIL D / SIL 3 capable designs. This level of CPU integration reduces the need for external processors and simplifies system validation.
System-Level advantages
By consolidating preprocessing, inference, and postprocessing within one adaptive SoC, designers can achieve:
For edge AI platforms operating in automotive, industrial, aerospace, or medical environments, these advantages directly translate into more reliable and scalable system architectures.
iWave has started rolling out samples of its iG-G77M Versal™ AI Edge Gen2 System on Module (SOM) and Development Kit to early access customers. The iG-G77M SOM is compatible with 2VE3858, 2VE3804, 2VE3558 and 2VE3504 devices. The platform is designed to accelerate evaluation, prototyping, and product development for next-generation edge AI systems. Engineering teams interested in evaluating the module for automotive, industrial, vision, robotics, or safety-critical applications can contact us for technical documentation, pricing, and availability details.
iWave Global is a leading embedded solutions provider specializing in FPGA and adaptive SoC-based System on Modules, single-board computers, and ODM services. With deep expertise in high-speed design, RF systems, AI acceleration, and safety-critical architectures, we enable customers to accelerate product development across automotive, industrial automation, aerospace & defense, medical, and high-performance embedded markets.
For more information, visit www.iwave-global.com or reach out to us at mktg@iwave-global.com
