3 citations · 3 across the 8 of their papers we have counts for
5 papers · 1 filter
Mirror Speculative Decoding: Breaking the Serial Barrier in LLM Inference
Nikhil Bhendawade, Kumari Nishu, Arnav Kundu +3
Speculative decoding accelerates LLM inference by using a draft model to look ahead, but gains are capped by the cost of autoregressive draft generation: increasing draft size elev…
FS-DFM: Fast and Accurate Long Text Generation with Few-Step Diffusion Language Models
Amin Karimi Monsefi, Nikhil Bhendawade, Manuel Rafael Ciosici +3
Autoregressive language models (ARMs) deliver strong likelihoods, but are inherently serial: they generate one token per forward pass, which limits throughput and inflates latency…
M2R2: Mixture of Multi-Rate Residuals for Efficient Transformer Inference
Nikhil Bhendawade, Mahyar Najibi, Devang Naik +1
Residual transformations enhance the representational depth and expressive power of large language models (LLMs). However, applying static residual transformations across all token…
Speculative Streaming: Fast LLM Inference without Auxiliary Models
Nikhil Bhendawade, Irina Belousova, Qichen Fu +3
Speculative decoding is a prominent technique to speed up the inference of a large target language model based on predictions of an auxiliary draft model. While effective, in appli…
Intelligent Assistant Language Understanding On Device
Cecilia Aas, Hisham Abdelsalam, Irina Belousova +20
It has recently become feasible to run personal digital assistants on phones and other personal devices. In this paper we describe a design for a natural language understanding sys…