Extreme Efficiency
Maximizing operations per watt for battery-constrained environments.
DEV SEC IT Research
Neural Processing Research
Next-generation hardware-software co-design for AI acceleration.
Capabilities
Core focus areas in Neural Processing
Hardware AccelerationQuantizationEdge AI
Benefits
What Neural Processing research delivers
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.
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.
Custom Kernels
Hand-optimized CUDA and OpenCL kernels for specific topologies.
Secure Enclaves
Executing neural inference within hardware-isolated trusted execution environments.
Methodology
Research & Implementation Methodology
- Profiling
- Graph Optimization
- Kernel Engineering
- Validation
Identifying bottlenecks in the current model execution graph.
Fusing operations and optimizing memory access patterns.
Writing bespoke hardware-specific kernels for the critical path.
Ensuring optimized outputs exactly match the baseline floating-point model.
Implementation
Technology Stack
We utilize modern 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
Offerings
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.
Discuss Your RequirementsCustom Kernel Development
Writing high-performance CUDA kernels for novel architectures.
Discuss Your RequirementsCompiler Integration
Building custom TVM/MLIR pipelines for new hardware.
Discuss Your RequirementsQuestions
Research FAQ
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.
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