Research and Development

AI Compiler Engineering & Optimization

At Nextkore, we specialize in AI Compiler design, optimization, and debugging helping enterprises, research teams, and hardware vendors accelerate performance across heterogeneous compute environments. Our expertise spans end-to-end compiler stack development, from IR (Intermediate Representation) optimizations to code generation and runtime scheduling, tailored for modern AI and ML workloads.

Why AI Compilers Matter

AI compilers are the backbone of efficient model execution. With the explosion of LLMs, transformers, and agentic inference systems, compiler performance and portability have become critical differentiators.
Traditional compiler pipelines are not optimized for:

  • Dynamic graph execution (as in PyTorch / JAX)
  • Quantization and mixed-precision support
  • Multi-device (GPU, TPU, NPU, FPGA) execution
  • Model partitioning and graph-level scheduling
  • Hardware-specific autotuning

This is where our expertise bridges the gap enabling teams to push the limits of compute efficiency, latency, and model portability.

Our Areas of Expertise

RIPE is not limited to storage collisions. Our team is actively exploring a broader range of storage-related issues that impact Ethereum's security and privacy, including:

  • Graph-level optimization: Operator fusion, pruning, quantization-aware scheduling
  • Memory & cache optimization: Dataflow scheduling, tensor tiling, buffer reuse
  • Performance tuning: Target-specific code generation for LLVM, MLIR, TVM, XLA, Triton
  • Parallelization: Automatic vectorization and threading for GPU/TPU clusters
  • Dynamic shape optimization for adaptive AI models
  • Custom frontend integration for new ML frameworks
  • Intermediate Representation (IR) extensions and transformations
  • Custom backend targeting for NPUs, FPGAs, and edge accelerators
  • Autotuner development using reinforcement learning or gradient-based search
  • Integration with ONNX, TorchScript, and TensorFlow XLA
  • Runtime profiling and tracing for model execution paths
  • Graph visualization tools for debugging IR transformations
  • Error localization and automatic rollback for optimization passes
  • Performance regression tracking across compiler releases
  • Integration with tools like LLVM PassManager, MLIR PassPipeline, and Perfetto
  • Compiler-runtime co-design for optimized scheduling and memory reuse
  • Integration with hardware abstraction layers (HALs) and runtime libraries
  • Quantization pipelines for post-training and QAT
  • Model migration between compilers (e.g., TVM ↔ TensorRT ↔ XLA)

Our Ecosystem Contribution

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Lets Collaborate

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