The field of natural language processing is moving towards more efficient language model architectures and hardware acceleration. Researchers are exploring new architectures, such as hybrid-architecture language models, and specialized hardware, like Language Processing Units (LPUs), to improve performance and reduce energy consumption. Notable papers include Jet-Nemotron, which achieves state-of-the-art accuracy while improving generation throughput, and Hardwired-Neurons Language Processing Units, which proposes a novel Metal-Embedding methodology to reduce photomask costs. Other papers, such as H2EAL and Flash Sparse Attention, focus on efficient inference and training methods for large language models, while APT-LLM introduces a comprehensive acceleration scheme for arbitrary precision LLMs.
Efficient Language Model Architectures and Hardware Acceleration
Sources
Jet-Nemotron: Efficient Language Model with Post Neural Architecture Search
Hardwired-Neurons Language Processing Units as General-Purpose Cognitive Substrates
TMA-Adaptive FP8 Grouped GEMM: Eliminating Padding Requirements in Low-Precision Training and Inference on Hopper
H2EAL: Hybrid-Bonding Architecture with Hybrid Sparse Attention for Efficient Long-Context LLM Inference
Flash Sparse Attention: An Alternative Efficient Implementation of Native Sparse Attention Kernel
APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration