Hardware Acceleration of Kolmogorov-Arnold Network (KAN) for Lightweight Edge Inference (2409.11418v1)

Abstract: Recently, a novel model named Kolmogorov-Arnold Networks (KAN) has been proposed with the potential to achieve the functionality of traditional deep neural networks (DNNs) using orders of magnitude fewer parameters by parameterized B-spline functions with trainable coefficients. However, the B-spline functions in KAN present new challenges for hardware acceleration. Evaluating the B-spline functions can be performed by using look-up tables (LUTs) to directly map the B-spline functions, thereby reducing computational resource requirements. However, this method still requires substantial circuit resources (LUTs, MUXs, decoders, etc.). For the first time, this paper employs an algorithm-hardware co-design methodology to accelerate KAN. The proposed algorithm-level techniques include Alignment-Symmetry and PowerGap KAN hardware aware quantization, KAN sparsity aware mapping strategy, and circuit-level techniques include N:1 Time Modulation Dynamic Voltage input generator with analog-CIM (ACIM) circuits. The impact of non-ideal effects, such as partial sum errors caused by the process variations, has been evaluated with the statistics measured from the TSMC 22nm RRAM-ACIM prototype chips. With the best searched hyperparameters of KAN and the optimized circuits implemented in 22 nm node, we can reduce hardware area by 41.78x, energy by 77.97x with 3.03% accuracy boost compared to the traditional DNN hardware.

• The paper introduces a hardware acceleration framework using algorithm-hardware co-design to efficiently map B-spline based Kolmogorov-Arnold Networks for edge inference.
• It details novel techniques—Alignment-Symmetry, PowerGap Quantization, TM-DV-IG, and sparsity-aware weight mapping—that deliver 41.78x area reduction, 77.97x energy savings, and a 3.03% accuracy boost.
• The findings promise practical deployment of large models on resource-constrained edge devices and pave the way for future research in energy-efficient AI acceleration.

Evaluating the Hardware Acceleration of Kolmogorov–Arnold Network for Edge Inference

The paper under examination explores the compelling notion of hardware acceleration for the Kolmogorov–Arnold Networks (KAN) to enable lightweight edge inference applications. The research is particularly significant given the growing demand for deploying complex models on edge devices constrained by resource availability and the need for real-time performance.

Overview of Kolmogorov–Arnold Networks (KAN)

KAN offers a paradigmatic shift from traditional deep neural networks (DNNs) by utilizing parameterized B-spline functions with trainable coefficients, promising a considerable reduction in the parameters necessary for achieving similar or superior performance. Nonetheless, despite this paradigmatic efficiency, the evaluation of B-spline functions introduces unique challenges for hardware implementation, which this paper seeks to address.

Hardware Acceleration Approach

The research employs a novel algorithm-hardware co-design approach that fuses algorithm-level techniques with advanced circuit-level innovations. The paper introduces:

• Alignment-Symmetry and PowerGap Quantization: This innovative method minimizes the hardware resources needed for LUTs, MUXs, and decoders, crucial for mapping B-spline functions effectively on edge devices.
• N:1 Time Modulation Dynamic Voltage Input Generator (TM-DV-IG): This technique optimizes the mixed time-voltage input generation, which significantly reduces on-chip area and power, resulting in enhanced MAC operation efficiency.
• KAN Sparsity-aware Weight Mapping Technique: Acknowledging the issues posed by IR-drop on bit lines, this technique enhances inference accuracy by redistributing weight considerations based on activation probability, thereby addressing physical implementation concerns.

Practical and Theoretical Implications

The practical implications of this paper are profound. The hardware optimization methods discussed provide significant improvements in power, area, and latency, making the deployment of large models on edge platforms more feasible. From a theoretical perspective, the development of KAN further extends the application of the Kolmogorov-Arnold theorem, presenting an innovative computational technique that challenges the conventional DNN frameworks.

Numerical Results and Validation

The paper presents impressive quantitative results. Specifically, the authors demonstrate a 41.78x reduction in area and a 77.97x reduction in energy compared to traditional DNN hardware, alongside a 3.03% accuracy boost. Such metrics not only validate the proposed approach but also underscore its potential applicability in real-world edge applications.

Future Directions

Moving forward, explorations into extending KAN to support a broader range of edge applications beyond current test scenarios are warrantable. Moreover, refining hardware implementations for different non-volatile memory technologies could present additional opportunities to further optimize power and area savings. Continual integration and evaluation in the presence of non-ideal effects will be essential to maintain the viability of KAN's scalability and reliability.

In conclusion, the paper paves substantial ground in the field of neural networks by advancing the hardware acceleration of KAN for edge processing. Its contributions establish an important foundation for future research endeavors aimed at optimizing AI computations for resource-limited environments.