통찰 - Computer Architecture - # FPGA Architecture for Deep Learning Acceleration

Field-Programmable Gate Array Architecture Enhancements for Efficient Deep Learning Inference

Q: How can the FPGA's on-chip memory blocks be enhanced with in-memory compute capabilities to provide thousands of parallel compute units at low cost?

To enhance FPGA on-chip memory blocks with in-memory compute capabilities, a key approach would be to integrate processing-in-memory technology. By adding low-cost reconfiguration circuitry to the block RAMs (BRAMs) in FPGAs, the BRAMs can be equipped with the ability to perform computations in addition to storing data. This enhancement would enable thousands of parallel compute units on the FPGA at a relatively low cost. By leveraging the flexibility and parallelism of the BRAMs, computations can be performed directly within the memory blocks, reducing data movement and latency, and increasing overall efficiency in processing large datasets commonly seen in deep learning workloads.

Q: What are the key challenges in integrating FPGAs and specialized DL accelerator chiplets using advanced packaging technologies, and how can these be addressed?

One of the key challenges in integrating FPGAs and specialized DL accelerator chiplets using advanced packaging technologies is ensuring seamless communication and synchronization between the different components within the package. The diverse nature of FPGA fabric, general-purpose processor cores, and specialized accelerator chiplets requires efficient network-on-chip (NoC) designs to facilitate high-speed data transfer and coordination. Additionally, optimizing power delivery and thermal management in a multi-die package can be complex, as different components may have varying power requirements and heat dissipation characteristics. To address these challenges, thorough system-level design and simulation are essential to ensure compatibility and performance across all integrated components. Advanced interposer technologies, such as passive interposers or embedded multi-die interconnect bridges, can be utilized to enable efficient communication between the FPGA and accelerator chiplets. Moreover, implementing intelligent power management schemes and thermal solutions can help mitigate issues related to power delivery and heat dissipation in the integrated package.

Q: What novel reconfigurable architectures beyond traditional FPGAs can be explored to combine the flexibility of FPGAs with the efficiency of coarse-grained DL accelerators?

Exploring novel reconfigurable architectures beyond traditional FPGAs can lead to the development of reconfigurable acceleration devices (RADs) that combine the flexibility of FPGAs with the efficiency of coarse-grained DL accelerators. One potential approach is to design RADs that integrate FPGA fabric with specialized DL accelerator cores and general-purpose processor cores in a single device. By incorporating a packet-switched network-on-chip (NoC) for efficient communication between these components, RADs can offer a versatile platform for implementing complex DL workloads. Additionally, advancements in processing-in-memory technology can be leveraged to enhance the compute capabilities of RADs. By integrating in-memory compute capabilities within the FPGA fabric, RADs can provide thousands of parallel compute units at a relatively low cost, enabling efficient processing of DL workloads. Furthermore, exploring innovative packaging technologies, such as advanced interposers or embedded multi-die interconnect bridges, can facilitate the integration of diverse components in RADs while ensuring seamless communication and coordination between them.

핵심 개념

FPGA architectures are evolving to enable higher performance and energy efficiency for deep learning inference workloads through customized compute units, memory hierarchies, and specialized hardware blocks.

초록

This article surveys the key innovations in FPGA architecture to better support deep learning (DL) inference acceleration. It first provides an introduction to FPGA architecture and highlights the unique strengths of FPGAs that make them suitable for DL inference, such as fine-grained programmability, spatial computing, and flexible I/Os.

The article then discusses different styles of DL inference accelerators on FPGAs, ranging from model-specific dataflow architectures to software-programmable overlay designs. It showcases examples of these accelerator designs and how they leverage the underlying FPGA architecture to achieve state-of-the-art performance.

Next, the article delves into the specific FPGA architecture enhancements being proposed to better support DL workloads. These include optimizing the logic blocks, arithmetic circuitry, on-chip memories, and integrating new DL-specialized hardware blocks into the FPGA fabric. The article also covers emerging hybrid devices that combine FPGA-like reconfigurable fabrics with coarse-grained DL accelerator cores.

Finally, the article highlights promising future research directions in the area of reconfigurable computing for DL, such as exploiting processing-in-memory capabilities of on-chip memories and exploring novel reconfigurable architectures that combine the strengths of FPGAs and specialized DL accelerators.

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arxiv.org

통계

"FPGAs can achieve 4x higher throughput at the same latency compared to a Nvidia V100 GPU for the ResNet-50 CNN model."
"Integrating DL-optimized tensor blocks in Intel Stratix 10 NX FPGAs improved the inference throughput of the HPIPE CNN accelerator by 4.8x, from 6,000 to 29,400 batch-1 inferences per second for MobileNet-V2."
"The NPU overlay on an Intel Stratix 10 FPGA achieves 11x higher performance than the Nvidia V100 GPU for various RNN workloads."

인용구

"FPGAs offer fine-grained hardware programmability which allows building customized compute datapaths and on-chip memory sub-systems that match exactly the application needs."
"FPGAs are spatial computing devices. This means that data does not have to move through a memory hierarchy of caches and register files for the computation to be performed, and compute cores do not have to communicate through memory."
"FPGAs provide a myriad of programmable input/output (IO) interfaces. These IOs can flexibly implement a wide variety of protocols with different electrical characteristics and timing specifications."

핵심 통찰 요약

Field-Programmable Gate Array Architecture for Deep Learning: Survey & Future Directions

by Andrew Boutr... 게시일 arxiv.org 04-17-2024

https://arxiv.org/pdf/2404.10076.pdf

Field-Programmable Gate Array Architecture for Deep Learning: Survey & Future Directions

더 깊은 질문

How can the FPGA's on-chip memory blocks be enhanced with in-memory compute capabilities to provide thousands of parallel compute units at low cost?

To enhance FPGA on-chip memory blocks with in-memory compute capabilities, a key approach would be to integrate processing-in-memory technology. By adding low-cost reconfiguration circuitry to the block RAMs (BRAMs) in FPGAs, the BRAMs can be equipped with the ability to perform computations in addition to storing data. This enhancement would enable thousands of parallel compute units on the FPGA at a relatively low cost. By leveraging the flexibility and parallelism of the BRAMs, computations can be performed directly within the memory blocks, reducing data movement and latency, and increasing overall efficiency in processing large datasets commonly seen in deep learning workloads.

What are the key challenges in integrating FPGAs and specialized DL accelerator chiplets using advanced packaging technologies, and how can these be addressed?

One of the key challenges in integrating FPGAs and specialized DL accelerator chiplets using advanced packaging technologies is ensuring seamless communication and synchronization between the different components within the package. The diverse nature of FPGA fabric, general-purpose processor cores, and specialized accelerator chiplets requires efficient network-on-chip (NoC) designs to facilitate high-speed data transfer and coordination. Additionally, optimizing power delivery and thermal management in a multi-die package can be complex, as different components may have varying power requirements and heat dissipation characteristics.
To address these challenges, thorough system-level design and simulation are essential to ensure compatibility and performance across all integrated components. Advanced interposer technologies, such as passive interposers or embedded multi-die interconnect bridges, can be utilized to enable efficient communication between the FPGA and accelerator chiplets. Moreover, implementing intelligent power management schemes and thermal solutions can help mitigate issues related to power delivery and heat dissipation in the integrated package.

What novel reconfigurable architectures beyond traditional FPGAs can be explored to combine the flexibility of FPGAs with the efficiency of coarse-grained DL accelerators?

Exploring novel reconfigurable architectures beyond traditional FPGAs can lead to the development of reconfigurable acceleration devices (RADs) that combine the flexibility of FPGAs with the efficiency of coarse-grained DL accelerators. One potential approach is to design RADs that integrate FPGA fabric with specialized DL accelerator cores and general-purpose processor cores in a single device. By incorporating a packet-switched network-on-chip (NoC) for efficient communication between these components, RADs can offer a versatile platform for implementing complex DL workloads.
Additionally, advancements in processing-in-memory technology can be leveraged to enhance the compute capabilities of RADs. By integrating in-memory compute capabilities within the FPGA fabric, RADs can provide thousands of parallel compute units at a relatively low cost, enabling efficient processing of DL workloads. Furthermore, exploring innovative packaging technologies, such as advanced interposers or embedded multi-die interconnect bridges, can facilitate the integration of diverse components in RADs while ensuring seamless communication and coordination between them.