Vec-LUT delivers up to 4.2x speedup over prior LUT methods for parallel ultra-low-bit LLM inference on edge devices by unifying lookups across tokens and adding cache-aware tensor layouts.
LUT tensor core: Lookup table enables efficient low-bit LLM inference acceleration,
2 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
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EVA is a vector-quantization hardware architecture that transforms LLM decoding from GEMV to GEMM via direct codebook dot products and conflict-free output buffering, claiming up to 11.17x speedup over prior lookup designs.
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Vec-LUT: Vector Table Lookup for Parallel Ultra-Low-Bit LLM Inference on Edge Devices
Vec-LUT delivers up to 4.2x speedup over prior LUT methods for parallel ultra-low-bit LLM inference on edge devices by unifying lookups across tokens and adding cache-aware tensor layouts.
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EVA: Accelerating LLM Decoding via an Efficient Vector Quantization Architecture
EVA is a vector-quantization hardware architecture that transforms LLM decoding from GEMV to GEMM via direct codebook dot products and conflict-free output buffering, claiming up to 11.17x speedup over prior lookup designs.