arXiv Computation and Language
Sep 7

Large Language Models with At Most One Spike per Neuron

The paper presents a spiking neural network (SNN) approach that uses time-to-first-spike (TTFS) coding to limit each neuron to at most one spike per time window, enabling energy-efficient large language models (LLMs). A reference-based strategy is introduced to encode the four core LLM components—embedding layers, layer normalization, attention-related operations, and dropout—allowing the construction of a fully TTFS-based SNN architecture trained end-to-end. Experiments on BERT and GPT-2 show performance comparable to artificial neural network (ANN) counterparts on natural language understanding and common-sense reasoning, while achieving a 1.5‑billion‑parameter spiking LLM and providing an estimate of spike-related energy consumption.

By Zhuoya Zhao, Parsa Omidi, Aref Jafari, Richard Naud
arXiv AI
Oct 5

Hardware-Native Joint Sparse-Quantization for Trillion-Scale Mixture-of-Experts

The paper introduces a hardware-software co‑design framework that compresses Mixture‑of‑Experts (MoE) model weights into low‑precision, hardware‑native sparse representations, enabling efficient execution on Sparse Tensor Cores (SpTCs). By relaxing discrete support selection through continuous reparameterization, the method jointly optimizes quantized weights and supports a router‑weighted reconstruction objective, achieving up to 4.35 percentage‑point gains in joint sparse‑quantization accuracy while retaining 96.09% of the original model’s performance. A custom grouped sparse GEMM kernel further boosts inference speed, outperforming NVIDIA’s baseline by up to 1.65× and reducing latency by up to 4.03× on B200 GPUs.

By Kwanhee Lee, Namhoon Lee, Dan Alistarh
arXiv AI
Aug 18

FluxBin: Flexible LUT-based Ultra-low-bit LLM Inference by Algorithm-Kernel Synergy

arXiv:2608. 15602v1 Announce Type: cross Abstract: While binary quantization theoretically promises extreme compression and acceleration for Large Language Models (LLMs), existing research often overlooks the necessity of specialized hardware kernels, thus failing to unleash the full acceleration potential due to persistent reliance on expensive floating-point arithmetic or runtime dequantization overheads.

By Qingyao Yang, Runming Yang, He Xiao, Wendong Xu, Junyu Chen, Haobo Liu, Chenchen Ding, Ruihan Hu, Yik-Chung Wu, Ngai Wong
arXiv Machine Learning
Sep 21

Elastic Threshold Attention: Learned Contextual Sparsity for Long-Context Decoding

Elastic Threshold Attention (ETA) is a trainable attention mechanism that dynamically predicts contextual thresholds from query representations, enabling selective pruning of KV cache tokens during long‑context decoding. By multiplicatively suppressing sub‑threshold logits during training, ETA avoids representation collapse and eliminates localized attention sinks, allowing a 1.45B model to match dense attention performance at roughly 85% training sparsity and 38% active decode density. At inference, a custom Triton kernel achieves up to 2.5× faster decoding on sequences up to 512K tokens, and an offline calibration step can further reduce compute by 27% by freezing per‑head thresholds.

By Themistoklis Haris, Henry Li, Maryam Karimzadehgan
arXiv Machine Learning
1d ago

SparseGDN: Chunk Sparse in Linear Attention

SparseGDN is a hardware‑aware framework that makes Gated Delta Networks (GDNs) more efficient for long‑context sequence modeling. It combines structured intra‑chunk approximation with an input‑dependent recurrent‑event selector, producing outputs for every token while reducing dense computation. The GPU implementation updates only selected events and reconstructs token outputs from compact state snapshots, cutting computation and memory traffic.

By Jiedong Zhuang, Peng Zhao, Jialin Tang, Haoji Hu