Quamba: Post-training quantization for selective state space models

Background

State Space Models (SSMs) are integral to numerous advanced technologies, including natural language processing, robotics, autonomous vehicles, and edge computing systems. As these applications increasingly demand real-time processing and intelligent decision-making on devices with limited computational resources, there is a pressing need for efficient deployment of SSMs on resource-constrained hardware platforms. Efficient model deployment enables broader accessibility and functionality of AI-driven applications in mobile computing, virtual and augmented reality, and other emerging fields where power and memory limitations are critical considerations.

However, existing approaches to quantizing SSMs face significant challenges that hinder their effectiveness on constrained devices. Traditional quantization methods lead to substantial accuracy degradation and computational overheads in SSM-based models, which are detrimental to performance in real-time applications. As a result, current techniques struggle to balance the trade-off between efficiency and performance, failing to optimize SSMs specifically for the unique demands of resource-limited environments. This gap highlights the need for innovative quantization strategies that can enhance memory and processing efficiency without compromising the accuracy and functionality of SSM-based technologies.

Technical description

Quamba is a groundbreaking quantization method designed specifically for State Space Models (SSMs), developed through a collaboration between the University of Texas at Austin and National Yang Ming Chiao Tung University. This technology addresses the critical challenge of deploying SSMs on resource-constrained hardware by enhancing efficiency without compromising performance.

Quamba implements static 8-bit per-tensor quantization and introduces two key innovations: the suppression of maximum input activation values to enable finer quantization and the use of the Hadamard transform for quantizing output activations in an outlier-free space. These advancements result in a significant reduction of memory usage by half, a 1.72x improvement in generation latency on Nvidia Orin Nano 8G, and only a minimal average accuracy drop of 1.27% in zero-shot tasks.

Utilizing established software frameworks such as PyTorch, CUTLASS, and Mamba, Quamba is versatile across various applications, including natural language processing, mobile computing, robotics, edge computing, image processing, and virtual and augmented reality.

What sets Quamba apart is its status as the first quantization algorithm tailored specifically for SSMs, marking a significant advancement in model quantization. Its ability to optimize both memory usage and processing speed while maintaining acceptable accuracy levels makes it exceptionally valuable for deploying advanced AI models in environments with limited resources, particularly in edge computing and mobile applications.

The rapid development timeline, with conception in early 2024 and initial experiments by mid-2024, underscores its potential for ongoing innovation and refinement. This combination of specialized design, efficiency gains, and industry interest differentiates Quamba as a leading solution for deploying sophisticated AI models in diverse, resource-constrained settings.