Cognitive Intelligence
Technical
Discuss Your Project
Neural Assets
Edge AI.
Model quantization, pruning, and hardware-specific optimization for deployment on edge devices with minimal latency.
TensorRTONNXTFLiteNPU
System Ready
Technical
Capabilities.
Model quantization (INT8/FP16) with minimal accuracy loss
Neural network pruning and knowledge distillation
ONNX Runtime optimization for cross-platform inference
TensorRT deployment on NVIDIA Jetson and embedded GPUs
TFLite and MediaPipe deployment for mobile and embedded devices
NPU and DSP backend targeting with custom operator development
Optimized for Silicon
Deploying AI on the edge requires deep hardware knowledge — we shrink models to fit constrained memory budgets while maintaining accuracy.
- Expertise in NVIDIA Jetson, Coral TPU, and ARM Ethos NPUs
- Custom AI accelerator integration and DSP backend targeting
- Secure on-device model protection against IP extraction
Core Focus
TensorRT, ONNX, TFLite — delivering measurable impact through deep technical expertise.
Engagement Model
From discrete consulting engagements to full turnkey delivery, we adapt to your program's specific needs and timeline.
Deep Tech FAQ
What model optimization techniques do you use?+
We apply quantization (INT8/FP16), pruning, knowledge distillation, and operator fusion to reduce model size by up to 80% while maintaining accuracy within 1-2% of the original.
Which edge hardware platforms do you target?+
We deploy on NVIDIA Jetson, Google Coral, ARM Ethos NPU, Qualcomm AI Engine, and custom FPGA accelerators, with ONNX Runtime and TensorRT as our primary inference engines.
ChipTalk.AI Advantage
Deploying AI on resource-constrained devices is ChipTalk's specialty. Our edge AI team has shipped quantized models on NPUs, DSPs, and FPGAs, achieving inference latencies measured in milliseconds on devices with under 2 W power budgets. We combine model compression expertise with deep hardware knowledge.
Related Engagements
01
Smart Camera Person Detection
Quantized a YOLOv8n model to INT8 with TensorRT and deployed it on Jetson Orin Nano, achieving <5 ms inference at 1080p with 95% of FP32 accuracy—all within a 7 W power budget.
02
Keyword Spotting on Cortex-M
Deployed a 35 KB keyword-spotting model on a Cortex-M4 DSP using CMSIS-NN, achieving 93% wake-word accuracy with 40 mW average power consumption for always-on voice activation.
We know the silicon—not just the framework. Our team has written custom ONNX operators for NPU acceleration, tuned quantization calibrators for per-tensor sensitivity, and debugged the RPC-level differences between PyTorch export and hardware runtime.
Relevant Tags:TensorRTONNXTFLiteNPU