Google’s TurboQuant Compression May Support Faster Inference, Same Accuracy on Less Capable Hardware
Google Research unveiled TurboQuant, a novel quantization algorithm that compresses large language models’ Key-Value caches ...
No mathematical seed. No deterministic shortcut. BBRES-RNG takes a fundamentally different approach to generating random numbers. Instead of relying on standard library algorithms or fixed ...
If Google’s AI researchers had a sense of humor, they would have called TurboQuant, the new, ultra-efficient AI memory compression algorithm announced Tuesday, “Pied Piper” — or, at least that’s what ...
As Large Language Models (LLMs) expand their context windows to process massive documents and intricate conversations, they encounter a brutal hardware reality known as the "Key-Value (KV) cache ...
Even if you don’t know much about the inner workings of generative AI models, you probably know they need a lot of memory. Hence, it is currently almost impossible to buy a measly stick of RAM without ...
Abstract: High-performance throughput is a critical issue in the hardware implementation of elliptic curve cryptography (ECC). This paper presents the construction of a high-speed ECC processor based ...
Abstract: Residue Number Systems (RNS) offer a promising approach to enhancing the computational efficiency of Fully Homomorphic Encryption (FHE). This paper proposes an efficient base conversion ...
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