Next-Generation Edge AI Services
Fast, secure, real-time intelligence on local hardware.
Inference doesn't belong in a data center when latency, bandwidth, or privacy demands local processing — Edge AI is fundamentally a semiconductor and embedded engineering problem.
This is not AI consulting; it is embedded engineering for machine-learning workloads.
EDGE AI SYSTEM ARCHITECTURE
V-X1
optional: metadata only
Sensor / Camera
→
Pre-processing
ISP · filtering · resize
→
AI Model
Accelerator
NPU · DSP · FPGA fabric
→
RISC-V / SoC control
scheduling · post-processing · decisions
→
Output
actuation · alert · metadata uplink
power · memory · thermal · latency budget
The whole pipeline lives inside one power and memory budget.
WHERE EDGE INTELLIGENCE RUNS
V-X2Cameras and vision systems
local CV inference where round-trip latency is unacceptable
Embedded devices
sensors and controllers with on-device models
Gateways
aggregation points running inference across multiple streams
Edge compute platforms
local servers and industrial compute at network edge
WHAT WE DO
V-X2AI model deployment
quantization, compilation, runtime integration for target hardware
Hardware-aware optimization
memory footprint, compute budget, thermal envelope, power
Embedded AI & computer vision
vision pipelines and on-device inference
Local inference architecture
latency, bandwidth reduction, privacy/data-locality
DEPLOYMENT FLOW
V-X31.
Trained Model
2.
Quantization
3.
Compilation
4.
Runtime Integration
5.
Hardware Optimization
6.
FPGA / Target Hardware
7.
Deployment
precision vs. accuracy trade-off
target-specific graph lowering
drivers, scheduling, memory plan
operator placement, buffer tiling, measured power
run it for real
CLOUD VS EDGE
V-X2| Criterion | Cloud inference | Edge inference |
|---|---|---|
| Latency | Network round-trip, variable | Milliseconds, deterministic |
| Bandwidth | Raw data upstream | Data stays local |
| Privacy | Data leaves premises | Data never leaves device |
| Power | Data-center budget | Device battery / thermal budget |
| Connectivity | Hard requirement | Operates offline |
| Compute | Elastic, large models | Fixed, constrained resources |
When two or more cloud rows are deal-breakers, inference moves to the edge — and becomes a hardware problem.
WHY SNOVA FOR EDGE
Edge AI performance is decided below the framework layer — in the silicon, the memory system, and the datapath.
SNOVA brings FPGA prototyping, RISC-V expertise, digital design, and SoC integration together to deliver real-time AI on real hardware. We optimize across the full stack — from model to microarchitecture — so your product meets its latency, power, and reliability targets.
Build Your Edge AI Solution
From model to silicon — real-time intelligence, built for your edge.