Google develops an "analog of Pied Piper" for an eightfold acceleration of neural network computations - ForkLog: cryptocurrencies, AI, singularity, the future

Froklog · 2026-03-26T12:53:36+00:00

Google's TurboQuant algorithm significantly enhances AI efficiency by reducing memory requirements and accelerating computations in neural networks, comparable to Pied Piper technology. It compresses data without loss of quality, achieving a sixfold memory reduction and eightfold speed increase.

Froklog

2026-03-26 12:53:36

Abstract generation in progress

# Google Develops “Pied Piper” Analog for Eightfold Speedup in Neural Network Computations

Google’s research division introduced TurboQuant — a memory compression algorithm for artificial intelligence. Users compared the development to Pied Piper technology from the series “Silicon Valley.”

TurboQuant is the new Pied Piper 🤣 pic.twitter.com/iMAYJs02zt

— Justin Trimble (@justintrimble) March 25, 2026

TurboQuant significantly reduces resource requirements for large language models and vector search systems.

AI operates with complex multidimensional arrays that store information about words or images. These data take up a lot of cache space and slow down response generation. Traditional compression methods require storing additional variables, often negating the benefits of optimization.

TurboQuant solves the memory overhead problem with two mechanisms. The first algorithm converts vectors into polar coordinates and compresses the main data. The second acts as a mathematical controller, using just one bit of memory to eliminate residual hidden errors.

Cloudflare CEO Matthew Prince compared the algorithm to Chinese model DeepSeek, which previously demonstrated high efficiency with minimal hardware costs.

This is Google’s DeepSeek. So much more room to optimize AI inference for speed, memory usage, power consumption, and multi-tenant utilization. Lots of teams at @Cloudflare focused on these areas. #staytuned https://t.co/hHoY4sLT2I

— Matthew Prince 🌥 (@eastdakota) March 25, 2026

Developers tested the technology on open models Llama, Gemma, and Mistral. The algorithm compressed the cache to three bits without losing answer quality. Memory consumption was reduced by at least six times, and processing speed on H100 GPUs increased eightfold.

The innovation requires no additional neural network training. According to the company, the technology will be integrated into search algorithms and their own AI products, including Gemini. The public presentation of the project will take place at the ICLR and AISTATS conferences in 2026.

Recall that on March 25, Google announced its plans to transition to post-quantum cryptography.

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