Hardware-Validated HLS Engines and Design-Time HBM Partitioning for AI Inference on AMD Alveo V80

Field-programmable gate arrays (FPGAs) with on-package high-bandwidth memory (HBM) are an attractive substrate for low-latency, precision-customizable AI inference, yet high-level synthesis (HLS) flows expose little control over how tensors are distributed across many independent memory channels. The AMD Alveo V80 spreads its nominal 820 GB/s across 64 pseudo-channels, each capped at 12.8 GB/s, so delivered bandwidth is governed by an explicit tensor-to-channel partitioning decision that existing framework-based flows do not surface. This paper presents a methodology for HLS-based inference on the V80 built on two coupled contributions: a design-time partitioning framework that assigns each tensor to on-chip (BRAM/URAM) storage, a single HBM channel, or a stripe across several channels according to its reuse and access pattern, and a three-stage HLS flow that separates functional baseline, unroll-and-partition throughput extraction, and precision-aware DSP packing. The methodology is developed through five model kernels—a parameterized GEMM, ResNet-18, ViT-Small, a BERT attention block, and GPT-2 Small—and realized as nine engines spanning GEMM (FP32, INT8, INT4), convolution, attention, layer normalization, SoftMax, pooling, and embedding lookup. All nine engines are synthesized in Vitis HLS 2024.2 and placed, routed, and executed on the physical Alveo V80 at 400 MHz (2.5 ns period), and every engine closes timing with positive worst-case slack. We report per-engine cycle counts, post-route utilization, and post-route dynamic-power estimates, and compare engine latency and energy against NVIDIA Titan RTX (GPU) and Intel Xeon W-3223 (CPU) baselines on identical kernels. A central result, confirmed on silicon, is that INT8 roughly halves the GEMM DSP58 footprint relative to FP32 (27 to 14 slices), whereas INT4 yields no further compute reduction and acts purely as a memory-placement lever. The full-model compositions, the roofline classification, and the tensor-to-channel placement framework are analytical, design-time results rather than end-to-end measured performance. The bandwidth-scaling premise underlying the placement framework is confirmed directly on the V80: a 1-to-64 channel sweep shows aggregate HBM bandwidth scaling near-linearly to within 5–13% of the device peak, and placing the GPT-2 LM-head operand on a single channel versus the eight the framework assigns it yields a measured 6.81× speedup—a direct on-board test of the striping decision. The resulting design guidelines target HLS practitioners working with HBM-equipped FPGAs.