Zen-Attention Framework

• Zen-Attention Framework is a compiler-based solution that fuses and tiles transformer attention operations to minimize DRAM accesses on AMD NPUs.
• It uses hardware-aware strategies like folding, DMA-based block transpose, and on-chip memory optimization to boost energy efficiency and reduce latency.
• Benchmarks demonstrate up to 4× latency reduction in attention blocks and up to 32% overall transformer latency improvement on consumer devices.

Zen-Attention Framework is a compiler-based optimization solution designed for deploying transformer model attention layers efficiently on AMD neural processing units (NPUs), specifically those implementing the XDNA architecture with a multi-level memory hierarchy and DMA-managed data movement. The framework systematically addresses DRAM bandwidth constraints and energy-efficiency goals by fusing, tiling, and optimizing tensor operations in the attention block to minimize off-chip memory accesses and maximize data reuse within hardware resources. The framework has demonstrated substantial reductions in latency for the attention mechanisms of transformer models on consumer-grade, bandwidth-limited devices.

1. Architectural Overview

Zen-Attention is built around a set of compiler-driven modules that leverage hardware-awareness to optimize transformer attention mapping. The framework ingests the ONNX computational graph, identifying chains of attention operations:

• Initial matrix multiplication $A = Q K^T$
• Optional bias and mask addition $A = A + B + M$
• SoftMax normalization
• Multiplication with value tensor $Z = \text{SoftMax}(A) V$

These are fused into a single, folded kernel wherever the hardware permits, reducing the off-chip DRAM accesses typical in layerwise implementations. The architecture features:

• Graph optimizer: Recognizes and fuses eligible attention operation sequences in the ONNX graph.
• Tiler and buffer allocator: Discovers all tiling options for query ($Q$), key ($K$), value ($V$), bias ($B$), and mask ($M$) with respect to the on-chip L1 memory, maximizing subvolume sizes while satisfying kernel constraints.
• Data transformation handlers: Implements folding-preserving transpose (block-wise DMA transpose at L2, followed by register-level shuffling within MatMul kernels) and padding (via DMA or predecessor layers) to satisfy tensor shape requirements.

Integration with the XDNA NPU architecture encompasses explicit management of the memory hierarchy: L1 per core, L2 shared among columns of cores, and L3 host DRAM, with direct control of DMA engines. This enables Zen-Attention to orchestrate buffer moves and kernel launches for maximal throughput and energy efficiency.

2. Attention Folding and Tiling Strategies

A major innovation in Zen-Attention is the principled use of operation folding and tiling to exploit on-chip memory hierarchies:

• Folding involves merging as many attention operations as can fit within L1 capacity into a unified kernel, quantified by a "folding_level": level-3 represents full fusion (matrix multiply, bias/mask, SoftMax, $V$ multiply), lower levels indicate partial fusion due to resource constraints.
• Tiling divides tensors along the sequence and context axes so that subvolumes fit into L1 buffers. The tiler enumerates and sorts valid subvolumes, prioritizing large spans for the query dimension.

This strategy ensures that intermediate results are retained in low-latency memory, drastically reducing roundtrip DRAM transfers, particularly for bandwidth-bound models with large sequence/context dimensions. The exclusion of separate transpose kernels and independent buffers, replaced by hybrid transpose mechanisms, further streamlines the compute pipeline.

The mathematical core of these operations is represented as:

$$A = Q K^T \ A = A + B + M \ SM(out) = \text{SoftMax}(A) \ Z = SM(out) V$$

Operation fusion, tiling, and folding are chosen dynamically based on input tensor sizes and NPU memory constraints.

3. Data Transformation: Transpose and Padding Mechanisms

Unlike conventional CPU/GPU frameworks, attention on NPUs must adapt tensor layouts explicitly. Zen-Attention employs:

• DMA-based block transpose at L2 (typically in 8×8 blocks), avoiding excess DRAM movement.
• Register-level shuffle within specialized MatMul kernels, allowing the transpose to be completed in situ during compute without separate kernel calls.
• Padding via hardware DMA, guaranteeing tensors meet minimal lane width requirements for NPU kernels. When hardware padding is not feasible, the framework utilizes earlier network layers to produce padded outputs.

This folding-preserving transpose approach eliminates overhead from standalone data transformation stages, allowing the attention block to proceed with optimal buffer utilization.

4. Design Space Exploration

The framework conducts systematic exploration over possible folding and tiling configurations:

• All valid tilings are enumerated and sorted to maximize the query subvolume within L1 constraints.
• Folding strategies are assessed for feasibility; highest folding_level is targeted, falling back as needed.
• Data motion strategies—including spatial unrolling of tiles across NPU cores and temporal splitting when tensor volumes exceed L2 capacity—are chosen to maximize kernel efficiency.

This design-space search enables adaptive optimization, catering to dynamic, heterogeneous workloads with varying input shapes and dimensions. This suggests the framework could accommodate evolving transformer architectures by further developing adaptive runtime tuning strategies.

5. Performance Evaluation

Zen-Attention has been benchmarked on representative transformer models, such as ViT-base-patch16, CLIP-patch32/CLIP-patch16/CLIP-Laion, and BERT:

Model Variant	Latency Gain in Attention Block	End-to-End Latency Improvement
ViT-base-patch16	up to 4×	up to 32%
CLIP variants	up to 4×	substantial (model-dependent)
BERT	modest (∼8%)	modest (attention not bottleneck)

Results confirm up to 4× lower attention block latency and up to 32% reduction in end-to-end transformer latency, largely attributable to reduced DRAM roundtrips and optimal buffer allocation. Compute-bound scenarios nevertheless benefit from improved DRAM bandwidth allocation, aiding application concurrency.

6. Use Cases and Applications

Zen-Attention has demonstrated practical value in:

• Computer vision models: ViT and CLIP variants with large patch/sequence sizes benefit from memory-efficient attention block execution.
• LLMs: BERT and similar architectures observe latency gains despite the attention block not being the primary bottleneck.
• Edge and consumer devices: Gaming consoles and laptops, which are resource-constrained, gain improvements in real-time inference latency and power draw.

Plausible implication is that these optimizations can generalize to other transformer-like models with dynamic attention and non-uniform tensor shapes, particularly in low-power deployment contexts.

7. Limitations and Prospects for Future Research

The principal limitations concern hardware memory constraints and data transformation complexity:

• L1 buffer size governs the extent of operation folding; large contexts or additional features (masking, extra padding) may necessitate partial fusion and reduce potential gains.
• Transpose and padding require sophisticated DMA orchestration; while folding-preserving transpose alleviates overhead, further hardware acceleration is a topic for exploration.
• Dynamic workload adaptation: As model architectures and input dimensions diversify, there is a need for more aggressive tiling algorithms, extensions to other neural layers, and improved runtime adaptivity to enable robust performance across workloads.

Future research directions include deepening kernel fusion, extending tiling/folding strategies, and integrating adaptive compiler heuristics for heterogeneous environments on NPUs.

Zen-Attention establishes a hardware-aware optimization paradigm for transformer attention mechanisms, facilitating efficient deployment and low-latency inference on resource-constrained devices. The detailed focus on folding, tiling, and judicious data movement represents a significant advancement in bridging model requirements and hardware capabilities (Deshmukh et al., 25 Aug 2025).