Model–Software–Hardware Co-Design · Edge AI · Custom ASICs
TrueASIC: The Chip Knows Its Own Software
XgenSilicon co-designs the model, compiler, runtime, and ASIC as a single system — so every transistor is shaped by the model it runs, and every software layer is shaped by the silicon beneath it.
Model, SW & HW optimized together, not in sequence
Per-Model
ASIC Architecture
Silicon shaped to each AI workload
Edge-First
Design Philosophy
Power · Latency · On-Device Privacy
// The Co-Design Advantage
One Feedback Loop. Two Sides of Silicon.
Traditional chip design runs model, software, and hardware through separate, sequential phases. XgenSilicon closes that gap — the model specs, compiler, runtime, and ASIC architecture co-evolve in a tight feedback cycle where every layer informs every other.
SW
Model Specification
Operator graphs, quantization targets, and latency budgets are encoded as constraints — not afterthoughts. The model becomes the specification the hardware is built around.
HW
HW Architecture Search
Proprietary AI methods traverse a large space of micro-architecture candidates in simulation, evaluating tradeoffs against the live model graph before a single gate is placed.
SW
HW-Aware Compilation
Code generation is derived for the exact silicon topology — not a generic target. The compiler and the chip share a common interface from day one.
HW
Silicon Implementation
RTL is generated from co-optimized block primitives. Model, software, and silicon are validated in lock-step — closing the loop so the next model iteration starts from a stronger baseline.
01
Model Specification
SW
AI model architecture, operator graph, and target accuracy/latency requirements are defined.
02
HW Architecture Search
HW
Proprietary AI methods explore the hardware space — guided by the model graph — to find the optimal architecture for the workload.
03
HW-Aware Compilation
SW
The compiler re-optimizes the model for the discovered hardware — code generation is specific to the target silicon, not a generic backend.
04
Silicon Implementation
HW
Custom ASIC is implemented using the co-optimized block library. Model, software, and hardware are validated together.
↺ continuous feedback loop
// Where XgenSilicon Competes
The applications where cloud inference is not an option
Autonomous vehicles, surgical robots, personal wearables, and on-device AI models share two hard constraints: latency that a datacenter round-trip cannot meet, and data that cannot leave the device. Off-the-shelf NPUs address latency but leave data sovereignty to the vendor. Custom silicon addresses both.
Cloud Inference
GPU datacenter
NPU
Off-the-shelf edge chip
XgenSilicon Custom ASIC
Design target
Inference latency
Round-trip to compute
Cloud Inference
Network + queue delay
NPU
Reduced latency
XgenSilicon Custom ASIC
On-device, purpose-built
Data stays on-device
No egress to external infra
Cloud Inference
Data sent to datacenter
NPU
Depends on vendor config
XgenSilicon Custom ASIC
Full data sovereignty (design target)
Firmware auditability
Customer can inspect full stack
Cloud Inference
Vendor-managed
NPU
Vendor-managed firmware
XgenSilicon Custom ASIC
Full-stack owned by customer
Workload specificity
Silicon matched to the model
Cloud Inference
General-purpose GPU
NPU
General-purpose NPU
XgenSilicon Custom ASIC
Co-designed for workload (design target)
Power envelope
Suitable for battery / embedded / wearable
Cloud Inference
Datacenter power draw
NPU
Mobile-class, tight for wearables
XgenSilicon Custom ASIC
Optimized per workload (design target)
Meets requirement
Partial / depends on config
Does not meet requirement
XgenSilicon ratings reflect design targets
// The Full Stack
One Platform, Both Sides of Silicon
Every layer of the XgenSilicon platform — model, compiler, runtime, block library, and ASIC — is built to be aware of every other layer. That's what Model–Software–Hardware Co-Design means in practice.
// Custom ASIC Die
Per-Model Silicon Architecture
Model-Aware Compiler
Built to understand the exact silicon it targets. The compiler is derived from the model graph — not adapted from a generic backend — so every operator maps to the hardware it was designed for.
Model-Guided Architecture Search
The AI model is the specification the hardware search runs against. Architecture candidates are evaluated against the live model graph — so the silicon is shaped by the workload from the first iteration.
Proprietary Silicon IP Library
A purpose-built library of silicon building blocks, each designed with its software interface in mind — so the compiler and the chip share a common language from day one.
Co-Designed Runtime
From model ingestion to on-chip execution, the runtime is built alongside the ASIC — not ported to it. Model, software, and silicon share a single interface with no translation layers between them.
The model shapes the silicon. The silicon shapes the model.
Most chip design flows treat the AI model as a fixed input — a spec handed to the compiler, which hands a spec to the hardware team. Each layer inherits constraints it had no part in setting. XgenSilicon inverts that: model architecture, software stack, and silicon are co-optimized as a single system, with every layer continuously informing every other.
Traditional
Traditional ASIC Design Flow
ModelModel Freeze
HWHW Specification
HWRTL Design
re-spin risk
SWCompiler Port
re-spin risk
SWCo-Validation
HWTape-out
The model is a fixed input. Hardware specs are set without visibility into compiler needs. The compiler is written without visibility into final silicon. Each layer inherits constraints it had no part in shaping — and re-spins are the cost of discovering that late.
XgenSilicon
Model-to-Silicon Co-Design Flow
ModelModel Specification
feedback
HWAI Architecture Search
feedback
SWModel-Aware Compiler
feedback
HWSilicon Implementation
feedback
SWCross-Layer Validation
feedback
HWTape-out
The model shapes the silicon. The silicon shapes the model. Model architecture, compiler, and hardware co-evolve — every layer has visibility into every other, so constraints are resolved at the point where they cost the least.
Model — AI model architecture
SW — Software / compiler
HW — Silicon / hardware
Re-spin risk
Cross-layer feedback
This diagram illustrates the structural difference in design methodology. It does not represent validated timelines or measured outcomes. XgenSilicon's Model–Software–Hardware Co-Design approach is a design-intent target; no silicon has been validated in production.
// Mission
Why Model-to-Silicon Co-Design Changes Everything
When model requirements, software architecture, and hardware specs are each locked in before the next layer can react, efficiency is lost at every boundary. XgenSilicon's platform keeps all three layers in continuous dialogue — so the compiler targets the exact silicon, and the silicon is built for the model.
We serve teams building autonomous systems, industrial robotics, personal wearables, and consumer electronics who need differentiated Edge AI products — and need them on a timeline that matters.
Reinforcement learning agents explore millions of micro-architecture candidates in simulation — evaluating power, area, and throughput tradeoffs against the live model graph — before a single gate is placed.
Explore the technical foundations of our System Software stack and custom ASIC platform — the Model–Software–Hardware Co-Design methodology in full detail.
Model–Software–Hardware Co-Design methodology and technical foundations of the TrueASIC platform.
// Get In Touch
Start Your Model-to-Silicon Journey
Tell us about your edge AI application. We'll show you how Model–Software–Hardware Co-Design — aligning the model, System Software stack, and custom ASIC from day one — can get you to silicon faster and more efficiently.
2445 Augustine Drive, Suite 150
Santa Clara, CA, USA
