Inactive
Simplifying IT
for a complex world.
Platform partnerships
- AWS
- Google Cloud
- Microsoft
- Salesforce
AI Engineering Services
Intelligence
Built into
Every Layer.
Silicon Patterns designs AI/ML accelerator chips, neural network IPs, and AI-assisted design workflows — enabling faster time-to-silicon for next-generation intelligent systems.
NPU
Neural Process Units
INT8
Quantized Inference
TOPS
Optimized Throughput
What We Deliver
AI Silicon — From Model to Chip
Silicon Patterns bridges the gap between AI model requirements and silicon constraints — designing custom NPUs, AI accelerators, and AI-assisted chip design flows that dramatically reduce development time and improve accuracy.
Custom NPU / AI accelerator architecture and RTL design
Neural network quantization, pruning, and silicon-aware model optimization
Systolic array, MAC array, and sparse tensor engine design
On-chip SRAM hierarchy, DMA, and NoC interconnect for AI workloads
AI-assisted RTL generation, lint, CDC, and design space exploration
Edge AI deployment: TensorFlow Lite, ONNX, and custom inference runtimes
AI Workload Support
CNN / ViTLLM InferenceObject DetectionTransformerRadar DSPSpeech
Precision & Quantization
FP32FP16 / BF16INT8INT4Mixed PrecisionSparse
Core Services
AI Engineering at Every Level
NPU & AI Accelerator Design
Custom neural processing units — MAC arrays, systolic engines, activation/pooling units, and on-chip memory hierarchies optimized for TOPS/W.
Model-to-Silicon Mapping
Network architecture co-design, layer-level hardware mapping, quantization-aware training, and inference runtime optimization for silicon targets.
AI-Assisted Chip Design
LLM-powered RTL generation, AI-driven design space exploration, automated constraint generation, and ML-based timing prediction.
AI SoC Integration
Integrating NPU IPs with CPU clusters, DSPs, memory subsystems, ISPs, and NOC fabric for full AI SoC design.
Edge AI Deployment
Optimizing pre-trained models for deployment on silicon targets — INT8/INT4 quantization, pruning, and compiler toolchain development.
AI Verification & Validation
ML model accuracy verification on silicon, golden model comparison, regression testing, and power/performance benchmarking.
Our Process
From AI Model to Production Silicon
01
Model Analysis
Analyze your AI model — ops breakdown, bandwidth requirements, latency targets, power budget, and hardware utilization study.
02
Architecture Design
NPU micro-architecture, dataflow selection (weight-stationary/output-stationary), SRAM sizing, and performance modeling.
03
RTL & IP Development
RTL design, functional verification, IP integration, and AI-assisted design flow validation with golden model comparisons.
04
Deployment & Tuning
Physical implementation, inference runtime integration, accuracy validation on silicon, and TOPS/W performance optimization.
Ready to Start?
Bring Intelligence to Your Next Chip
From NPU architecture to deployment-ready edge AI silicon — talk to our AI engineers today.
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