Tensor Manipulation Unit (TMU): Reconfigurable, Near-Memory Tensor Manipulation for High-Throughput AI SoC (2506.14364v1)

Abstract: While recent advances in AI SoC design have focused heavily on accelerating tensor computation, the equally critical task of tensor manipulation, centered on high,volume data movement with minimal computation, remains underexplored. This work addresses that gap by introducing the Tensor Manipulation Unit (TMU), a reconfigurable, near-memory hardware block designed to efficiently execute data-movement-intensive operators. TMU manipulates long datastreams in a memory-to-memory fashion using a RISC-inspired execution model and a unified addressing abstraction, enabling broad support for both coarse- and fine-grained tensor transformations. Integrated alongside a TPU within a high-throughput AI SoC, the TMU leverages double buffering and output forwarding to improve pipeline utilization. Fabricated in SMIC 40nm technology, the TMU occupies only 0.019 mm2 while supporting over 10 representative tensor manipulation operators. Benchmarking shows that TMU alone achieves up to 1413 and 8.54 operator-level latency reduction compared to ARM A72 and NVIDIA Jetson TX2, respectively. When integrated with the in-house TPU, the complete system achieves a 34.6% reduction in end-to-end inference latency, demonstrating the effectiveness and scalability of reconfigurable tensor manipulation in modern AI SoCs.

• The paper introduces a novel TMU that reduces inference latency by up to 34.6% through optimized tensor manipulation operators.
• It employs a RISC-inspired execution model with a reconfigurable masking engine to support diverse tensor operations in AI SoCs.
• System-level strategies such as double buffering and tensor prefetching are used to enhance data movement and overall throughput.

Tensor Manipulation Unit (TMU): High-Throughput AI SoC Design

This paper presents the Tensor Manipulation Unit (TMU), a reconfigurable, near-memory hardware block designed to optimize data-movement-intensive (DMI) operations in AI Systems-on-Chip (SoCs). It addresses crucial performance bottlenecks in modern AI workloads by efficiently handling tensor manipulation (TM) operators, which are traditionally underexplored compared to compute-intensive operations.

Introduction to Tensor Manipulation

Tensor manipulation (TM) operators are pivotal in contemporary neural networks, functioning predominantly to orchestrate data movement with minimal computation. Common TM operations such as transpose, reshape, and non-maximum suppression constitute substantial portions of inference latency due to their extensive interaction with memory hierarchies.

Figure 1: The average operator-level latency when running (a) YOLOv3/v8+pre/postproc and (b) EDSR on an NVIDIA RTX 3080.

The paper identifies that up to 40.62% of end-to-end inference latency can be attributed to TM operators as demonstrated in benchmarks on NVIDIA RTX 3080 GPUs (Figure 1). TMU aims to alleviate these latencies by streamlining memory data transfers.

TMU Architecture and Execution Model

The TMU integrates closely with a Tensor Processing Unit (TPU) within an AI SoC, utilizing a RISC-inspired execution model enabling support for diverse TM operators. With both coarse-grained and fine-grained manipulations, TMU processes tensor streams efficiently using a unified address abstraction.

Figure 2: Graphical representation of typical TM operators adopted in state-of-the-art neural networks.

Figure 3: Generic execution model for TM.

Figure 4: (a) System architecture integrating the proposed TMU and TPU. Two TMUs are deployed to support tensor prefetching. (b) Network architecture of EDSR.

As illustrated in Figures 2 and 3, TMU employs a novel address generator and reconfigurable masking engine to optimize both data routing and element-wise processing. Integrated into a comprehensive SoC architecture (Figure 4), it supports operator-level configurations through a matrix-based approach, accommodating transformations such as Img2col and PixelShuffle dynamically.

System-Level Strategies

To minimize latency and optimize throughput, TMU employs double buffering, tensor prefetching, and output forwarding strategies as shown in Figure 5.

Figure 5: (a) Non-prefetch strategy. (b) Tensor prefetch strategy. (c) Tensor prefetch and Output forwarding strategy.

These strategies enhance collaboration between the TMU and TPU, effectively overlapping data movement with computation to mitigate performance bottlenecks in neural network pipelines.

Experimental Evaluation

The TMU showcases substantial latency improvements across various neural network implementations, including YOLOv8 and EDSR, achieving up to a 34.6% reduction in inference latency. It's synthesized using SMIC $40~\mathrm{nm}$ technology, occupying only $0.019~\mathrm{mm^2}$ of silicon, proving its viability in compact, high-throughput AI SoCs.

Figure 6: TMU's microarchitecture and dataflows. (a) Fine-grained TM. (b) Coarse-grained TM. (c) TM with element-wise processing.

Figure 7: (a) Address generator for coarse-grained TM operators. (b) Reconfigurable masking engine (RME) for fine-grained TM operators.

The TMU outperforms conventional CPUs and GPUs by orders of magnitude, demonstrating that advanced architectural support for TM operators is critical for next-generation AI systems.

Conclusion

TMU provides significant architectural advancements for AI SoCs by addressing the underexplored domain of tensor manipulation. Its novel approach to high-throughput data movement, combined with computational efficiencies, sets a precedent for upcoming AI hardware developments. The integration of TMU not only decreases inference latency but ensures adaptability across diverse and evolving neural network workloads. The findings suggest considerable potential for extending TMU frameworks to broader AI applications, paving the way for scalable and reconfigurable AI SoC architectures.