Forward-Looking Statements. This page contains forward-looking statements, including projections regarding technology performance, design timelines, market opportunity, and competitive positioning. These statements reflect XgenSilicon's current expectations and design intent and are not guarantees of future results. Actual outcomes may differ materially due to technical, market, and execution risks. No silicon has been validated in production. All performance figures, timelines, and comparisons are targets or design-intent projections unless explicitly stated otherwise. This material is for informational purposes only and does not constitute an offer to sell or a solicitation of an offer to buy any securities.
Investor Relations
XgenSilicon designs custom edge AI ASICs — chips built for specific AI workloads in autonomous systems, industrial robotics, personal wearables, and consumer electronics. We work at the intersection of AI software and semiconductor design.
The Edge AI Silicon Revolution
The AI compute landscape is undergoing a fundamental shift. As AI models move from centralized cloud infrastructure to distributed edge devices, the demand for purpose-built, power-efficient silicon is exploding.
General-purpose GPUs and NPUs leave significant performance and efficiency headroom on the table for specific model architectures. Custom ASICs, designed specifically for a target model, can deliver 10–100× improvements in TOPS/W — but historically required years of development and tens of millions of dollars.
XgenSilicon changes this equation. By co-designing model, software, and hardware from day one — combining a proprietary AI-driven architecture search, a model-to-silicon co-optimizing compiler, and a purpose-built silicon IP library — we design custom ASICs in 3–6 months, making purpose-built edge AI chips accessible to autonomous systems, industrial robotics, personal wearables, and a wider range of applications.
Edge AI chip market by 2030
Projected TAM across autonomous systems, industrial robotics, wearables, IoT
Efficiency potential vs. general-purpose
TOPS/W headroom available with custom ASIC
Target time to silicon
vs. 12–24 months with traditional EDA flow
Application breadth
Autonomous · Industrial Robotics · Wearables · IoT · Consumer
Why XgenSilicon
Four core reasons why XgenSilicon represents a compelling investment opportunity in the edge AI semiconductor space.
Massive Market Opportunity
The edge AI chip market is projected to exceed $50B by 2030, driven by autonomous systems, robotics, and on-device AI. XgenSilicon is positioned at the inflection point.
Proprietary Co-Design Advantage
Our SW↔HW co-design methodology — from ML model to GDSII — covers the full stack. A proprietary HW Foundation Library (custom NoC, accelerator blocks, and memory subsystem), combined with a hardware-aware compiler, is designed to produce chips with PPA characteristics that neither pure-software nor pure-hardware vendors are set up to deliver.
Broad Application Reach
From autonomous vehicles and industrial robotics to consumer wearables and smart infrastructure — any edge AI application is a potential customer.
Deep Technical Team
Our founders and leads bring decades of combined experience in semiconductor design, ML compilers, and systems architecture from leading research and industry institutions.
Full vertical integration
Every abstraction boundary between stack layers leaks efficiency. GPU vendors own only the silicon. ASIC vendors own the chip but borrow the software. XgenSilicon owns all five layers — and co-designs model, software, and hardware from day one.
GPU / NPU Vendor
Off-the-shelf accelerator
ASIC + 3rd-party SW
Custom chip, borrowed stack
XgenSilicon
Full vertical integration
AI Model
Operator graph · Quantization · Accuracy targets
ML Compiler
Fusion · Tiling · Scheduling · Kernel gen
Runtime & Toolchain
Execution engine · Profiler · Debugger
Proprietary Silicon IP Library
Custom silicon building blocks · Memory subsystem
ASIC Silicon
RTL · Physical design · Tape-out
Every abstraction boundary between layers is a potential efficiency loss. XgenSilicon owns the full stack — from the ML compiler down through the runtime, a proprietary silicon IP library, and the ASIC silicon itself — so model, software, and hardware are designed as one system.
Stack ownership reflects XgenSilicon's platform design intent. End-to-end integration has not yet been validated in a production tape-out.
Technology Moat
Traditional ASIC Development
- 18–36 month design cycles
- $10M–$50M NRE costs
- Manual EDA tool flows
- Siloed software & hardware teams
- Limited design space exploration
OUR APPROACHXgenSilicon ASICs
- 3–6 months to silicon (target)
- Lower NRE cost (target)
- Purpose-built for target AI model
- Unified Model–SW–HW co-design
- Proprietary silicon IP library
General-Purpose Accelerators
- Off-the-shelf, no customization
- Suboptimal PPA for specific models
- No model-specific optimization
- One-size-fits-all architecture
- Limited competitive differentiation
Connect With Our Team
We welcome conversations with investors who share our conviction that the future of AI runs on custom silicon at the edge. Reach out to learn more about XgenSilicon's technology, roadmap, and investment opportunity.
For investor inquiries, partnership discussions, or to request our technical white paper, please contact us directly.
XgenSilicon Inc.