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Found 6 papers, 1 papers with code
PipeRAG: Fast Retrieval-Augmented Generation via Algorithm-System Co-design
Retrieval-augmented generation (RAG) can enhance the generation quality of large language models (LLMs) by incorporating external token databases.
Chameleon: a heterogeneous and disaggregated accelerator system for retrieval-augmented language models
The heterogeneity ensures efficient acceleration of both LM inference and retrieval, while the accelerator disaggregation enables the system to independently scale both types of accelerators to fulfill diverse RALM requirements.
Co-design Hardware and Algorithm for Vector Search
Vector search has emerged as the foundation for large-scale information retrieval and machine learning systems, with search engines like Google and Bing processing tens of thousands of queries per second on petabyte-scale document datasets by evaluating vector similarities between encoded query texts and web documents.
Switch Spaces: Learning Product Spaces with Sparse Gating
In order for learned representations to be effective and efficient, it is ideal that the geometric inductive bias aligns well with the underlying structure of the data.
MicroRec: Efficient Recommendation Inference by Hardware and Data Structure Solutions
MicroRec accelerates recommendation inference by (1) redesigning the data structures involved in the embeddings to reduce the number of lookups needed and (2) taking advantage of the availability of High-Bandwidth Memory (HBM) in FPGA accelerators to tackle the latency by enabling parallel lookups.
Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning
Federated learning (FL) is a novel machine learning setting that enables on-device intelligence via decentralized training and federated optimization.
