Hardware-Aware AI
Optimization Platform

Transform LLMs into ultra-efficient executables for specialized chips, eliminating cloud dependency and reducing costs by 10x.

Model

LLaMA-4-7B

Optimization Pipeline
1
Distillation to 1.2B parameters
2
Ternary weight conversion (-1,0,+1)
3
Bend compilation with SIMD instructions

Result on

STM32H750 (DSP)

0.2mJ/inference

Our Competitive Advantage

Existing Solutions (Competitors)

API Wrappers (LangChain, LlamaIndex)

Specialize generic models for specific tasks but depend entirely on cloud infrastructure and don't optimize for local/edge hardware.

Local Inference Frameworks (llama.cpp, TensorRT-LLM)

Run LLMs on GPUs/CPUs but aren't optimized for DSPs, FPGAs or specialized chips.

Our Unique Differentiators

Hardware-Aware Optimization

We compile models for specific parallel architectures (DSPs, FPGAs, NPUs). Example: LLaMA-3 can run on a microcontroller STM32H750 with just 2MB RAM after optimization.

Cloud-Independent

While competitors depend on APIs (OpenAI) or expensive GPUs, we enable local inference on low-cost chips.

10x Lower Cost

A ternarized (BitNet) model consumes 0.2mJ/inference on a DSP vs 3mJ on a GPU.

Chip Integration Technology

How specialized chips integrate with our optimization pipeline

GPUs

Accelerate matrix operations (MatMul) via CUDA/Triton

FP8/Tensor Cores

DSPs

Process ternary operations (-1,0,+1) efficiently

BitNet on TI C7x

FPGAs

Run custom kernels in Verilog

MLIR for Xilinx Versal

NPUs

Execute INT4/INT8 optimized operations

Winograd kernels

Key Technical Insight

This is not just a "wrapper layer" over existing models.

It's a complete recompilation of the model for the target chip architecture, using:

  • Distillation: Reduces model size
  • Quantization: Converts weights to efficient formats (ternary)
  • HW-Specific Compilation: Generates native code (CMSIS-DSP for ARM)

Example Workflow

  1. Client has a LLaMA-4-7B model for STM32H750 (DSP)
  2. Our platform:
    • Distills to 1.2B parameters
    • Converts weights to ternary (-1,0,+1)
    • Compiles with Bend using SIMD
  3. Result: Runs on DSP with no cloud/GPU dependency

Comparison With Existing Solutions

See how we outperform traditional approaches

Feature Our Project Wrappers (LangChain) Infer. Local (llama.cpp)
DSP Optimization Yes (BitNet+HVM) No No (CPU/GPU only)
Low Cost/Energy 0.1mJ/inf. (DSP) 3mJ/inf. (Cloud) 1.5mJ/inf. (GPU)
Cloud Independence Complete Dependent Partial
FPGA/NPU Support Yes (MLIR/Verilog) No No

Unique Use Cases

Where our solution outperforms alternatives

Industrial IoT

A factory sensor with STM32H7 analyzes text (NLP) without internet connectivity.

STM32H7 DSP

Medical Devices

A pacemaker with TI C7x DSP detects anomalies in real-time via TinyBERT.

TI C7x DSP

Defense Systems

Drones with Xilinx Zynq FPGAs process computer vision offline.

Xilinx Zynq FPGA

Summary of Our Technology

We transform generic LLMs into ultra-efficient executables for specialized chips, eliminating cloud dependency and reducing costs by 10x.

Why We're Unique

  • No competitor does hardware-aware compilation for DSPs/FPGAs
  • Technical advantage: Combination of BitNet + Bend + MLIR

Next Steps

Prototype Development

DeepSeek-V3 on STM32H750

Industry Partnerships

STMicro, NVIDIA

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