PENSA: A Spatial Accelerator for BitNet b1.58 Inference on an HBM-equipped AMD Alveo FPGA

Ternary large language models (tLLMs) replace general multiplication in dominant linear layers with sign-controlled accumulation, shifting hardware bottlenecks toward packed-weight delivery, reduction, and operator occupancy. \textsc{PENSA} is an end-to-end FPGA accelerator for BitNet~b1.58~2B4T model on an AMD Alveo U50\texttrademark. Two row-partitioned ternary matrix engines consume packed TQ1\_0 weights from eight independent high-bandwidth memory (HBM) pseudo-channels, each processing 1,280 ternary weights per cycle through sign selection and integer reduction. The sequencer, attention, and vocabulary-projection units remain resident on the FPGA, while two-stream interleave scheduling overlaps sequences across the persistent pipeline to form the Penta-Unit. At 104.1~MHz, \textsc{PENSA} achieves 58.09~tok/s at context 128 and 24.60~tok/s at context 2048, corresponding to 2.14--3.14$\times$ the throughput of a same-model Xeon E-2356G baseline. The resulting system demonstrates end-to-end FPGA execution of 2B-scale BitNet topology without modifying its model structure.