Ternary Quantization

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4 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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Latest papers 19

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SSR: Sparse Segment Reduction for Ternary GEMM Acceleration
Oct 6, 2026Adeline Pittet, Shien Zhu, Valérie Verdan +1Ternary Quantization LLM Inference Acceleration
Systems Group, ETH Zurich, Switzerland
Lost in the bf16 Cast: Exporting Ternary Language Models Can Revert Most Low-Learning-Rate Code Changes
Oct 6, 2026Avichal Sahai, Nishant Raj, Animesh SrivastavaLLM Quantization Ternary Quantization
Ofbusiness
Fiona: Accelerating FHE Inference with Packing-Aware Ternary Weights
Sep 28, 2026Yiteng Peng, Zhibo Liu, Dongwei Xiao +1Mixed-Precision Quantization Efficient Neural Network Inference
The Hong Kong University of Science and Technology · State Key Laboratory of Novel Software Technology, Nanjing University
Baseline Shape Decides the Verdict: A Controlled Re-Examination of Ternary Language Models at 60K Parameters
Sep 24, 2026Gautam VeldandaTernary Quantization Quantization-Aware Training
Independent Researcher
Breaking the 1.58-bit Barrier for Ternary LLMs
Sep 14, 2026Evangelos Georganas, Alexander Heinecke, Pradeep DubeyTernary Quantization LLM Compression
Intel Corporation
Scaling Post-Training Ternarisation to Qwen3-8B Capability Retention, Reproduction, Lossless Packing, and Packed Execution
Sep 10, 2026Anirudh Malik, M Sparsh Mehra, Poojith DevanLLM Quantization Ternary Quantization
OneBit AI
Post-Training Ternarization of Qwen3-4B Capability, Effective Bit Budget, Storage Compression, and Deployment
Sep 2, 2026Anirudh Malik, M Sparsh Mehra, Poojith DevanLLM Quantization Ternary Quantization
OneBit AI
QTEA: Ternary LLMs with Sparse Residual Salient Weight and By-Column Optimization
Aug 31, 2026Yipin Guo, Arun M George, Jie Fu +3LLM Quantization Ternary Quantization
University of Notre Dame
Tied Trit-Planes: Constraining PTQTP to a Uniform Nine-Level Quantizer, with a Persistent Folded Format for Disk-Streamed Mixture-of-Experts Serving
Aug 9, 2026Matteo GrellaTernary Quantization Mixture-of-Experts Inference
Crisis24
Attend to Your Own Thoughts: Breaking the Barrier for Post-Training Quantization of Reasoning LLMs through the Lens of 1.58-Bit Quantization
Aug 2, 2026Shigeng Wang, Chao Li, Yangyuxuan Kang +2LLM Quantization Ternary Quantization
Intel Labs China
ExTernD: Expanded-Rank Ternary Decomposition Ternary LLM PTQ with Accuracy Approaching Any Quantization Level
Jul 15, 2026Chethan Reddy G. PLLM Quantization Ternary Quantization
ELiTeFormer: An Efficient Transformer for FPGAs
Jul 4, 2026Victor Agostinelli, Nicolas Bohm Agostini, Antonino TumeoLLM Quantization Ternary Quantization
Oregon State University Corvallis, Oregon, USA · Pacific Northwest National Laboratory Richland, Washington, USA
CAT-Q: Cost-efficient and Accurate Ternary Quantization for LLMs
Jun 25, 2026Shigeng Wang, Chao Li, Yangyuxuan Kang +2LLM Quantization Ternary Quantization
1Intel Labs China
Ternary Mamba: Grouped Quantization-Aware Training of W1.58A16 State Space Models
Jun 16, 2026Ramprasath Ganesaraja, Sahil Dilip Panse, Swathika NLLM Quantization Ternary Quantization
EdgeVerve Systems Limited
TWLA: Achieving Ternary Weights and Low-Bit Activations for LLMs via Post-Training Quantization
Jun 11, 2026Zhixiong Zhao, Zukang Xu, Zhixuan Chen +3Mixed-Precision Quantization LLM Quantization
Houmo AI, China · 2Southeast University, Nanjing, China
FTerViT: Fully Ternary Vision Transformer
May 20, 2026Szymon Ruciński, Pietro Bonazzi, Engin Türetken +3Ternary Quantization Vision Transformer
CSEM, Neuchâtel, Switzerland · ETH Zürich, Zurich, Switzerland
A Geometric Analysis of Sign-Magnitude Asymmetry in a ReLU + RMSNorm Block under Ternary Quantization
May 18, 2026Lei DongLLM Quantization Ternary Quantization
Independent Researcher
FairyFuse: Multiplication-Free LLM Inference on CPUs via Fused Ternary Kernels
Apr 22, 2026Fei Zuo, Xiaoyan Xi, Quanyi Zeng +2Efficient Neural Network Inference Ternary Quantization
BA TechWorks · Peking University
ButterflyMoE: Compression-Scalable Ternary Experts via Structured Butterfly Orbits
Jan 20, 2026Aryan KarmoreTernary Quantization Mixture-of-Experts Language Models
Indian Institute of Information Technology, Nagpur

Oct 6, 2026, cs.LGJ/K move · Enter open · S save

SSR: Sparse Segment Reduction for Ternary GEMM Acceleration

Adeline Pittet, Shien Zhu, Valérie Verdan, Gustavo Alonso

Systems Group, ETH Zurich, Switzerland

Ternary Quantization LLM Inference Acceleration

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Large Language Models (LLMs) require substantial computational resources, limiting their deployment on resource-constrained hardware. Ternary LLMs mitigate these demands through weight quantization via ternary values, achieving significant compression often with 50-90% sparsity. However, existing approaches have limitations: methods optimized for ternary weights, such as BitNet, redundant segment reduction (RSR), and its improved version RSR++, do not exploit sparsity structures, while conventional sparse formats neglect ternary characteristics, foregoing dual optimization opportunities. In this paper, we introduce Sparse Segment Reduction (SSR), a ternary matrix multiplication method designed to accelerate the inference of ternary LLMs and general Ternary Weight Networks (TWNs). SSR has a dedicated optimized ternary data format and an algorithm that systematically exploits sparsity patterns through computation trees that scale with the sparsity. SSR provides theoretical gains with asymptotically faster inference than RSR++ for sparsity above 50%, while practical evaluations reveal performance improvements across all sparsity levels. Evaluation results show that SSR achieves 2.1-11.3x speedup over RSR++ on ternary GEMM with 45-95% sparsity. Furthermore, SSR achieves 3.5-6.3x end-to-end speedup and 4.9% of memory saving over RSR++ on the Llama-3 1B model inference.

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