Mission-Critical Hardware Use Cases.
Discover how FairView’s unified 2nm compute and 16,384-bit co-packaged HBM4 memory substrate eliminates the memory wall across autonomous mobility, humanoid robotics, frontier generative AI clusters, and aerospace sensor processing.
Autonomous Mobility & 3D World Models
Embodied AI & Humanoid World Models
Frontier LLM Training & Real-Time Inference
Aerospace & Phased-Array Radar Systems
Real-Time 3D Occupancy Perception at Zero Frame Drop.
Next-generation L4/L5 autonomous vehicle architectures fuse 12+ 8K HDR surround cameras, 4+ solid-state LiDARs, and radar point clouds into continuous 3D Neural Radiance Fields (NeRFs) and occupancy networks.
Why Conventional MPU/Memory Architectures Fail
Discrete PCIe-attached MPUs and GDDR7 memories introduce severe latency jitter (>40ms) and memory bandwidth throttling (1.5 TB/s limit), causing unsafe perception latency spikes and dropped sensor frames.
How Stallion + Gallium Delivers 10x Advancements
Stallion S100 + Gallium HBM4 provides 16.0 TB/s of co-packaged memory directly on the compute substrate, executing full multi-modal sensor fusion and motion planning in <8.5ms deterministically within an in-vehicle 450W–700W liquid-cooled envelope.
Normalized Performance vs Legacy Industry Baseline.
| Workload Metric | FairView Silicon (Stallion + Gallium) | Industry Baseline (Discrete PCIe / GDDR7 / Legacy Memory) |
|---|---|---|
| Sensor Ingestion Latency | 8.2 ms (Deterministic) | 38.5 ms (Discrete PCIe) |
| Memory Bus Throughput | 16.0 TB/s (Co-Packaged) | 1.536 TB/s (GDDR7 Base) |
| 3D NeRF Frame Rate | 120 FPS Real-Time | 28 FPS (Dropped Frames) |
| Energy per Sensor Token | 0.9 pJ / bit | 3.6 pJ / bit |
On-Chassis Multimodal Intelligence with Zero Cloud Reliance.
Humanoid robots and physical AI agents require 70B+ parameter Vision-Language-Action (VLA) foundation world models to interact dynamically with unpredictable physical environments in real time.
Why Conventional MPU/Memory Architectures Fail
Offloading reasoning to cloud servers introduces unpredictable network latency (100–500ms) and security hazards, while traditional mobile edge chips lack the memory capacity and bandwidth to host frontier foundation models locally.
How Stallion + Gallium Delivers 10x Advancements
FairView packages 512 GB of high-bandwidth Gallium memory directly with the Stallion MPU die, enabling humanoids to carry massive multimodal foundation models on-chassis, running a 1,000 Hz real-time motor control loop at 0.9 pJ/bit battery efficiency.
Normalized Performance vs Legacy Industry Baseline.
| Workload Metric | FairView Silicon (Stallion + Gallium) | Industry Baseline (Discrete PCIe / GDDR7 / Legacy Memory) |
|---|---|---|
| Local VLA Model Capacity | 70B+ Parameters (Local) | 7B Parameters (Edge limit) |
| Kinematics Control Loop | 1,000 Hz Real-Time | 50 Hz (Cloud Lag) |
| Battery Energy Draw | 0.9 pJ / bit I/O | 2.8 pJ / bit |
| Multimodal Vision FPS | 90 FPS Continuous | 18 FPS |
Eliminating the Memory Wall at 99.2% Tensor ALU Utilization.
Next-generation generative AI clusters train multi-trillion parameter Mixture-of-Experts (MoE) neural architectures, requiring high-frequency token routing and massive parameter streaming across distributed nodes.
Why Conventional MPU/Memory Architectures Fail
Memory bandwidth limitations starve modern compute engines, dropping actual tensor ALU utilization down to 30–45% and wasting millions of dollars in idle datacenter power.
How Stallion + Gallium Delivers 10x Advancements
Operating across an ultra-wide 16,384-bit substrate interface, Gallium delivers 16.0 TB/s continuous throughput, maintaining 99.2% tensor ALU saturation and slashing training epochs by up to 2.8×.
Normalized Performance vs Legacy Industry Baseline.
| Workload Metric | FairView Silicon (Stallion + Gallium) | Industry Baseline (Discrete PCIe / GDDR7 / Legacy Memory) |
|---|---|---|
| Tensor ALU Utilization | 99.2% Sustained Saturation | 38.4% (Memory Starved) |
| Per-Socket Memory BW | 16.0 TB/s Continuous | 3.200 TB/s (Legacy GPU) |
| MoE Token Routing Latency | < 8 ns Substrate Interconnect | 28 ns |
| Sparse FP8 Throughput | 9.44 PFLOPS / Socket | 0.40 PFLOPS |
High-Throughput Synthetic Aperture Radar Signal Reduction.
Airborne and orbital synthetic aperture radar (SAR) platforms process multi-gigahertz raw radio-frequency streams, demanding instant matrix reduction and neural beamforming under severe size, weight, and power (SWaP) constraints.
Why Conventional MPU/Memory Architectures Fail
Legacy signal processing systems require discrete multi-chip boards that overheat, introduce signal degradation, and lack the bandwidth to process wideband phase-array antennas in real time.
How Stallion + Gallium Delivers 10x Advancements
The monolithic 2nm Stallion S100 with co-packaged Gallium HBM4 performs real-time RF matrix beamforming and image formation on a single substrate, cooled by direct liquid metal TIM and protected by real-time SECDED hardware ECC.
Normalized Performance vs Legacy Industry Baseline.
| Workload Metric | FairView Silicon (Stallion + Gallium) | Industry Baseline (Discrete PCIe / GDDR7 / Legacy Memory) |
|---|---|---|
| SAR Image Formation Rate | Real-Time (< 50 ms) | 12.4 seconds (Post-process) |
| RF Channel Ingestion | 64 Concurrent RF Streams | 8 Concurrent Streams |
| Thermal Envelope Temp | < 65°C under 100% Load | 95°C (Thermal Throttling) |
| Memory Reliability | SECDED + Link ECC | Basic Single ECC |
Design Your Next-Gen Compute Architecture.
Engage directly with FairView silicon architects to evaluate register-level floorplans, thermal cold plate simulation models, and early silicon evaluation boards for your deployment.