XAMBA makes Mamba and Mamba-2 state-space models run up to 4.8x faster on an Intel AI PC NPU by replacing CumSum and ReduceSum with matrix operations and activations with piecewise linear approximations.
NITRO: LLM Inference on Intel Laptop NPUs
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abstract
Large Language Models (LLMs) have become essential tools in natural language processing, finding large usage in chatbots such as ChatGPT and Gemini, and are a central area of research. A particular area of interest includes designing hardware specialized for these AI applications, with one such example being the neural processing unit (NPU). In 2023, Intel released the Intel Core Ultra processor with codename Meteor Lake, featuring a CPU, GPU, and NPU system-on-chip. However, official software support for the NPU through Intel's OpenVINO framework is limited to static model inference. The dynamic nature of autoregressive token generation in LLMs is therefore not supported out of the box. To address this shortcoming, we present NITRO (NPU Inference for Transformers Optimization), a Python-based framework built on top of OpenVINO to support text and chat generation on NPUs. In this paper, we discuss in detail the key modifications made to the transformer architecture to enable inference, some performance benchmarks, and future steps towards improving the package. The code repository for NITRO can be found here: https://github.com/abdelfattah-lab/nitro.
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XAMBA: Enabling Efficient State Space Models on Resource-Constrained Neural Processing Units
XAMBA makes Mamba and Mamba-2 state-space models run up to 4.8x faster on an Intel AI PC NPU by replacing CumSum and ReduceSum with matrix operations and activations with piecewise linear approximations.