Using Unmodified DRAM, MVDRAM Accelerates Matrix-Vector Multiplication for Efficient LLM Inference

On March 31, 2025, researchers introduced MVDRAM, a novel system designed to accelerate matrix-vector multiplication (GeMV) operations for low-bit large language models using unmodified DRAM, addressing a critical bottleneck in generative AI inference within hardware architecture.

Even with low-bit quantised models, matrix-vector multiplication (GeMV) is a critical bottleneck in large language model inference. While Processing-Using-DRAM (PUD) offers potential to repurpose DRAM for high-throughput GeMV operations, it incurs significant overheads that limit its effectiveness. This paper introduces MVDRAM, the first practical system to accelerate GeMV for low-bit LLM inference using unmodified DRAM. By exploiting data sharing patterns and mathematical linearity in GeMV, MVDRAM eliminates input pre-arrangement and output bit-transposition costs, addressing PUD’s overhead challenges without requiring DRAM modifications.

In the rapidly evolving landscape of generative artificial intelligence (AI), researchers are constantly seeking ways to optimize computational efficiency while minimizing resource requirements. A recent breakthrough, detailed in academic research, introduces a novel approach to accelerating matrix-vector multiplication (GeMV) operations using unmodified dynamic random-access memory (DRAM). This innovation, known as MVDRAM, has the potential to significantly enhance the performance of low-bit quantized large language models (LLMs), particularly on resource-constrained devices.

The Innovation: MVDRAM and Its Promise

Matrix-vector multiplication is a fundamental operation in machine learning, especially in neural network computations. In the context of generative AI, these operations are often performed at reduced bit-widths to conserve memory and computational resources—a technique known as low-bit quantization. While this approach reduces the model’s footprint and energy consumption, it also introduces challenges in maintaining computational efficiency.

MVDRAM addresses these challenges by repurposing standard DRAM chips to perform computations directly within their memory arrays. Unlike traditional processing-in-memory (PiM) techniques that require specialized hardware modifications, MVDRAM leverages the inherent analog properties of existing DRAM technology. By intentionally violating manufacturer-specified timing parameters during data operations, researchers have demonstrated the ability to execute highly parallel bitwise computations within the memory itself.

This approach eliminates the need for data movement between memory and processing units, a major bottleneck in traditional computing architectures. The result is a significant reduction in latency and energy consumption, particularly for low-bit GeMV operations that are central to generative AI workloads.

Overcoming Limitations: How MVDRAM Works

The key challenge in using DRAM for computation lies in its fundamental design limitations. Unlike specialized memory circuits or custom chips designed for in-memory computing, standard DRAM lacks the ability to move data across different columns within a subarray. This limitation has historically hindered efforts to perform complex computations directly within memory.

To overcome this, MVDRAM employs two core operations: RowCopy and majority-of-X (MAJX). The RowCopy operation transfers data between rows by exploiting incomplete bitline precharging, while MAJX computes the majority voting of X cells connected to the same bitline. These operations can be performed in parallel across all columns in a DRAM bank, enabling massive computational throughput—up to 65,536 bitwise operations simultaneously.

By carefully designing algorithms that align with these capabilities, researchers have successfully demonstrated the feasibility of performing GeMV operations directly within unmodified DRAM chips. This innovation unlocks new possibilities for efficient computation and avoids the costs and complexities associated with developing specialized hardware.

The Key Concept: Leveraging DRAM’s Inherent Capabilities

The success of MVDRAM hinges on its ability to repurpose existing DRAM technology without requiring any hardware modifications. This approach contrasts with other PiM techniques that rely on adding computation logic near memory arrays or using specialized memory circuits. By focusing on the analog operational characteristics of standard DRAM, researchers have unlocked a previously untapped resource for computational efficiency.

This breakthrough has significant implications. For developers working on generative AI models, MVDRAM offers a practical path to improving inference performance without investing in expensive hardware upgrades. It also opens new possibilities for deploying advanced AI capabilities on devices with limited memory bandwidth and power budgets, such as smartphones or edge computing devices.

A Promising Future for Generative AI

As the demand for generative AI continues to grow, so does the need for more efficient and accessible computational resources. MVDRAM represents a promising step toward achieving these goals by innovatively leveraging existing technology. By reducing reliance on specialized hardware, this approach democratizes access to high-performance computing while minimizing environmental impact through reduced energy consumption.

MVDRAM remains a research prototype for now, but its potential applications are vast. As the technology matures, it could play a pivotal role in advancing the next generation of generative AI models, enabling faster inference speeds and more efficient resource utilization across a wide range of devices.

In summary, MVDRAM exemplifies the kind of creative thinking needed to overcome the computational challenges of modern AI. By repurposing existing technology and pushing the boundaries of what is possible with standard hardware, researchers have opened new avenues for innovation in generative AI and beyond.