Semiconductor 'brain' combines Transformer's intelligence and Mamba's efficiency
Analysis of post-Transformer fashions and proposal of a problem-solving acceleration system. Credit: The Korea Advanced Institute of Science and Technology (KAIST)

As latest synthetic intelligence (AI) fashions’ capability to grasp and course of lengthy, complicated sentences grows, the need for brand new semiconductor applied sciences that may concurrently increase computation pace and reminiscence efficiency is rising.

Amidst this, a joint analysis workforce that includes KAIST researchers and worldwide collaborators has efficiently developed a core AI semiconductor “brain” know-how based mostly on a hybrid transformer and Mamba construction, which was carried out for the primary time on the earth in a kind able to direct computation contained in the reminiscence, leading to a four-fold enhance within the inference pace of enormous language fashions (LLMs) and a 2.2-fold discount in energy consumption.

A analysis workforce led by Professor Jongse Park from KAIST School of Computing, in collaboration with Georgia Institute of Technology within the United States and Uppsala University in Sweden, developed PIMBA, a core know-how based mostly on AI reminiscence semiconductor (PIM, processing-in-memory), which acts because the mind for next-generation AI fashions.

The analysis is to be offered on the 58th International Symposium on Microarchitecture (MICRO 2025) and is at the moment available on the arXiv preprint server.

Currently, LLMs resembling ChatGPT, GPT-4, Claude, Gemini, and Llama function based mostly on the transformer mind construction, which sees all the phrases concurrently. Consequently, because the AI mannequin grows and the processed sentences grow to be longer, the computational load and reminiscence necessities surge, main to hurry reductions and excessive vitality consumption as main points.

To overcome these issues with transformer, the lately proposed sequential memory-based Mamba construction launched a way for processing data over time, rising efficiency. However, reminiscence bottlenecks and energy consumption limits nonetheless remained.

Professor Park Jongse’s analysis workforce designed PIMBA, a brand new semiconductor construction that straight performs computations contained in the reminiscence in an effort to maximize the efficiency of the transformer–Mamba hybrid mannequin, which combines the benefits of each transformer and Mamba.

While present GPU-based programs transfer information out of the to carry out computations, PIMBA performs calculations straight inside the with out shifting the information. This minimizes information motion time and considerably reduces .

As a outcome, PIMBA confirmed as much as a 4.1-fold enchancment in processing efficiency and a mean 2.2-fold lower in vitality consumption in comparison with present GPU programs.

More data:
Wonung Kim et al, Pimba: A Processing-in-Memory Acceleration for Post-Transformer Large Language Model Serving, DOI: 10.1145/3725843.3756121 On arXiv. DOI: 10.48550/arxiv.2507.10178

Journal data:
arXiv


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Semiconductor ‘mind’ combines transformer’s intelligence and Mamba’s efficiency (2025, October 17)
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