Why not just use standard TensorRT?

Standard compilers often fail to optimize novel or non-standard neural architectures. We build the custom kernels they miss.

How much faster can you make it?

Depending on the baseline, we often achieve 2x to 10x throughput improvements through careful co-design.

Do you work with edge devices like Raspberry Pi?

Yes. We specialize in quantizing and deploying models to resource-constrained ARM devices, maximizing inference speed without heavy batteries.

What is kernel fusion?

Kernel fusion is a technique where we combine multiple sequential mathematical operations into a single GPU/NPU kernel, drastically reducing memory bandwidth bottlenecks.

Can you optimize training as well as inference?

Absolutely. We develop custom CUDA kernels for the backward pass as well, significantly reducing cloud compute costs during the training phase.

DEV SEC IT RESEARCH

Neural Processing Research

Next-generation hardware-software co-design for AI acceleration.

The Engineering Challenge of Neural Processing

We research how to make AI run faster and cheaper by designing software that perfectly matches the physical layout of modern neural processing units.

Developing a Proof of Concept is easy. Engineering a reliable, scalable, and secure system for production is incredibly difficult. Our research is directly targeted at solving enterprise-grade problems within the Neural Processing space.

Extreme Efficiency

Maximizing operations per watt for battery-constrained environments.

Ultra-Low Latency

Microsecond response times for real-time critical systems.

Cost Reduction

Slashing cloud compute bills by optimizing inference pipelines.

Edge Deployment

Running complex models on standard IoT hardware.

Core Research Offerings

How we apply our foundational research to solve complex enterprise problems.

Model Compression

Shrinking large models for edge deployment without losing accuracy.

Custom Kernel Development

Writing high-performance CUDA kernels for novel architectures.

Compiler Integration

Building custom TVM/MLIR pipelines for new hardware.

Research & Implementation Methodology

Profiling

Identifying bottlenecks in the current model execution graph.

Graph Optimization

Fusing operations and optimizing memory access patterns.

Kernel Engineering

Writing bespoke hardware-specific kernels for the critical path.

Validation

Ensuring optimized outputs exactly match the baseline floating-point model.

Technology Stack

We utilize state-of-the-art frameworks and hardware.

Compilation

TVM
MLIR
XLA
TensorRT

Languages

CUDA
C++
Triton
Assembly

Techniques

INT8/INT4 Quantization
Pruning
Knowledge Distillation

Targets

NVIDIA NPUs
Apple Neural Engine
ARM Ethos

Research FAQ

Explore Other Research Areas

Deep Learning Deepspace Network 6G Network Architecture Quantum Computing System Engineering Low Level Engineering

Ready to Operationalize Neural Processing?

Partner with our research division to build sovereign, secure, and highly optimized systems for your enterprise.