KATANA: A Fast, Low-Power Mapping of Kalman Filters onto Edge NPUs for Real-Time Tracking

2026-06-12

AI Summary

This work addresses the challenge of deploying real-time, low-power multi-object tracking on edge devices, where conventional Kalman filters struggle to efficiently leverage general-purpose neural processing units (NPUs). For the first time, the authors present an end-to-end deployment of both linear and extended Kalman filters (LKF/EKF) on a commercial low-power NPU without requiring custom hardware. They introduce three algebraic graph rewriting techniques—precomputation of negative projection matrices, fusion of static-shape tensors, and block-diagonal batch parallelization—to ensure all computations execute entirely within the NPU’s matrix engine. Evaluated on an Intel Core Ultra Series 2 platform, the optimized batch EKF achieves 223.35 FPS at 13.43 W, while LKF reaches 408.73 FPS at 14.05 W, reducing dynamic energy consumption by up to 97.9% compared to CPU-based implementations.

Abstract

State estimation is the closed-loop core of every real-time tracking system, from radar surveillance and counter-UAV defense to autonomous driving and robotics. These deployments run on edge platforms, where defense systems mount on vehicles and drones, and civilian pipelines live on cars and handheld devices. Here, every additional watt of compute erodes mission duration or operational range. Two hard constraints follow: each new measurement must be fused before the next control cycle, and the total compute must fit within a strict battery and thermal power envelope. The Linear and Extended Kalman Filters (LKF, EKF) are dominant estimators on these systems, but today they execute almost exclusively on CPUs, which serialize multi-object tracking (MOT) updates, or on custom FPGA/ASIC accelerators that lengthen design cycles. Contemporary AI-PC SoCs, like the Intel Core Ultra Series 1 and 2, integrate a low-power, data-parallel Neural Processing Unit (NPU). We therefore ask whether the Kalman filter can be mapped onto this existing matrix engine to meet real-time and low-power budgets simultaneously, avoiding a dedicated accelerator and keeping the CPU and GPU free for primary workloads. We present KATANA, an NPU-aware optimization framework delivering the first end-to-end mapping of the LKF and EKF onto a commercial NPU, alongside a cross-platform characterization on shipping AI-PC silicon. KATANA applies three algebraic graph rewrites: subtract-to-add reformulation via a precomputed negative-projection matrix H_neg, static-shape tensor fusion, and block-diagonal batched parallelization, ensuring 100% of operations execute on the DPU matrix engine. On the Series 2, the optimized batched EKF reaches 223.35 FPS at 13.43 W active power, and the LKF reaches 408.73 FPS at 14.05 W, delivering up to a 97.9% reduction in dynamic energy versus the CPU implementation.

Problem

• Kalman Filter
• Edge NPU
• Real-Time Tracking
• Low-Power Computing
• Multi-Object Tracking

Innovation

• Kalman Filter
• Neural Processing Unit (NPU)
• Edge AI
• Low-Power Acceleration
• Batched Parallelization