Research Paper

Core Innovation

This paper presents NLI, a novel method that formulates nonlinear function approximation as a dynamic programming problem to achieve globally minimal interpolation error. Unlike prior work focusing on linear layers, NLI efficiently approximates nonlinear layers like SiLU, RMSNorm, and Softmax, enabling hardware-friendly implementation and significant speed improvements.

Why It Matters

Large language models face deployment challenges due to high computational and memory demands, especially from nonlinear operations. This solution reduces inference costs and latency without sacrificing accuracy, enabling broader adoption of LLMs in resource-constrained environments. It scales across various AI applications requiring efficient nonlinear computations.

Potential Customers & Pain Points

• Cloud providers – High inference cost and latency
• AI hardware manufacturers – Need efficient nonlinear computation units
• Enterprises deploying LLMs – Resource constraints limiting model use
• AI developers – Balancing model accuracy and speed

Business Model

Licensing the NLI algorithm and hardware design to AI chip manufacturers and cloud service providers; offering SDKs and APIs for AI developers to integrate NLI into existing workflows.

Competitive Landscape

• NVIDIA TensorRT
• Intel OpenVINO
• Google TPU
• Graphcore IPU

Implementation Challenges

• Integration complexity with diverse LLM architectures
• Hardware adoption requiring redesign or compatibility
• Competition from established AI acceleration platforms

Validation Strategy

• Benchmark NLI engine against state-of-the-art nonlinear approximation methods on standard LLM tasks
• Pilot deployments with cloud providers to measure cost and latency improvements
• Collaborate with hardware partners to validate integration and performance gains