With the ever increasing prevalence of neural networks and the upheaval from the language models, it is time to rethink neural acceleration. Up to this point, the broader research community, including ourselves, has disproportionately focused on GEneral Matrix Multiplication (GEMM) operations. The supporting argument was that the large majority of the neural operations are GEMM. This argument guided the research in Neural Processing Units (NPUs) for the last decade. However, scant attention was paid to non-GEMM operations and they are rather overlooked. As deep learning evolved and progressed, these operations have grown in diversity and also large variety of structural patterns have emerged that interweave them with the GEMM operations. However, conventional NPU designs have taken rather simplistic approaches by supporting these operations through either a number of dedicated blocks or fall back to general-purpose processors. This work sets out to challenge the conventional wisdom in neural accelerator design and explore the architecture of an on-chip companion, dubbed Tandem Processor, that complements the rather optimized GEMM unit in neural accelerators. This processor needs to be specialized to keep up with the GEMM unit; and yet needs to be programmable to address the (1) structural and (2) operational variations. To strike a balance between specialization and programmability, on the one hand, we specialize its memory access logic with a novel ISA/microarchitecture that alleviates the register file and its associated load/store operations. On the other hand, the calculations of the non-GEMM layers are only supported through primitive arithmetic/logic vector operations. Therefore, programmability is offered at the mathematical level. The enhancements due to the specialization of the memory access logic in the Tandem Processor and its tight integration with the GEMM unit sustain the throughput and the utilization of the neural accelerator. Comprehensive evaluations of the proposed design based on the end-to-end execution of seven diverse DNNs including emerging language models show significant performance improvements and energy reduction enabled by leveraging the Tandem Processor. We provide the RTL code that is synthesizable both for FPGA and ASIC implementations in addition to the associated compiler as part of the open-source GeneSys project (https://actlab-genesys.github.io/). We also present the chip floorplan and post-layout analysis. This work is the result of 10 years of effort in building real NPUs that support end-to-end neural network execution.

@inproceedings{GhodratiKXMKAWK24,
  title = {Tandem Processor: Grappling with Emerging Operators in Neural Networks},
  author = {Soroush Ghodrati and Sean Kinzer and Hanyang Xu and Rohan Mahapatra and Yoonsung Kim and Byung Hoon Ahn and Dong Kai Wang and Lavanya Karthikeyan and Amir Yazdanbakhsh and Jongse Park and Nam Sung Kim and Hadi Esmaeilzadeh},
  booktitle = {ASPLOS},
  year = {2024},
  doi = {10.1145/3620665.3640365}
}