AMD’s High-Efficiency Compressor Trees Boost FPGA Efficiency, Versatility in Machine Learning

Researchers from AMD Research and Tampere University have developed high-efficiency compressor trees for AMD Field Programmable Gate Arrays (FPGAs), significantly improving their efficiency. These compressor trees are integral to high-fan-in dot product computations, prevalent in signal processing and machine learning. The team’s design reduces the Look-Up Table (LUT) footprint by 45% for a plain summation and 46% for a terminal accumulation, allowing for operations well above 500MHz. The compressor trees were developed in the context of the new AMD Versal fabric, a reconfigurable fabric that allows for flexible and efficient design solutions.

What are High-Efficiency Compressor Trees for AMD FPGAs?

High-efficiency compressor trees are a crucial component in the latest AMD Field Programmable Gate Arrays (FPGAs). These trees are integral to high-fan-in dot product computations, which are prevalent in application domains such as signal processing and machine learning. The diverse set of data formats used in machine learning presents a challenge for creating flexible, efficient design solutions. Ideally, a dot product summation is composed of a carry-free compressor tree followed by a terminal carry-propagate addition. On FPGAs, these compressor trees are constructed from generalized parallel counters, with their architecture closely tied to the underlying reconfigurable fabric.

The work of Konstantin J. Hoßfeld, Hans Jakob Damsgaard, Jari Nurmi, Michaela Blott, and Thomas B. Preußer from AMD Research and Tampere University reviews known counter designs and proposes new ones in the context of the new AMD Versal fabric. They have developed a compressor generator featuring variable-sized counters, novel counter composition heuristics, explicit clustering strategies, and case-specific optimizations like logic gate absorption.

How do these Compressor Trees Improve Efficiency?

The compressor trees developed by the team significantly improve efficiency. Compared to the Vivado default implementation, the combination of such a compressor with a novel highly efficient quaternary adder reduces the LUT (Look-Up Table) footprint across different bit matrix input shapes by 45% for a plain summation and by 46% for a terminal accumulation. This improvement comes at a slight cost in critical path delay, but still allows an operation well above 500MHz.

The team demonstrated the aptness of their solution with examples of low-precision integer dot product accumulation units. This shows that the compressor trees can handle a wide range of data formats, making them suitable for diverse applications in signal processing and machine learning.

What is the AMD Versal Fabric?

The AMD Versal fabric is a new reconfigurable fabric from AMD. It is the context in which the team developed their new counter designs for the compressor trees. The architecture of the compressor trees is closely tied to this underlying reconfigurable fabric, which allows for flexibility and efficiency in design solutions.

The Versal fabric is part of AMD’s latest FPGAs, which are reconfigurable integrated circuits. These circuits can be programmed to perform a wide range of tasks, making them versatile for various applications. The high-efficiency compressor trees developed by the team are a significant component of these FPGAs, contributing to their flexibility and efficiency.

What are the Key Concepts and Terms?

Several key concepts and terms are essential to understanding the work of the team. A compressor tree is a component of a dot product summation, which is a common operation in signal processing and machine learning. It is composed of a carry-free compressor tree followed by a terminal carry-propagate addition.

A generalized parallel counter is a component of the compressor tree, and its architecture is closely tied to the underlying reconfigurable fabric of the FPGA. The team developed a compressor generator featuring variable-sized counters, novel counter composition heuristics, explicit clustering strategies, and case-specific optimizations like logic gate absorption.

The LUT footprint refers to the amount of space required in the Look-Up Table, a component of the FPGA. The team’s compressor tree design reduces this footprint by 45% for a plain summation and by 46% for a terminal accumulation.

Who are the Key People Involved?

The key people involved in this work are Konstantin J. Hoßfeld, Hans Jakob Damsgaard, Jari Nurmi, Michaela Blott, and Thomas B. Preußer. They are researchers from AMD Research and Tampere University. Their work on high-efficiency compressor trees for AMD FPGAs contributes significantly to the field of reconfigurable logic and FPGAs.

Hans Jakob Damsgaard and Jari Nurmi also acknowledge funding by the European Union’s Horizon 2020 Research and Innovation Program under the Marie Skłodowska-Curie Grant Agreement No. 956090 (APROPOS – Approximate Computing for Power and Energy Optimisation).

What is the Impact of this Work?

The work of the team has a significant impact on the field of reconfigurable logic and FPGAs. Their high-efficiency compressor trees improve the efficiency of AMD’s latest FPGAs, reducing the LUT footprint and allowing for operations well above 500MHz. This makes the FPGAs more versatile and suitable for a wide range of applications, particularly in signal processing and machine learning.

The team’s work also contributes to the development of the new AMD Versal fabric, a reconfigurable fabric that allows for flexible and efficient design solutions. Their compressor generator, with its novel counter designs and optimization strategies, is a significant component of this fabric.

The team’s work is also funded by the European Union’s Horizon 2020 Research and Innovation Program, indicating its relevance and importance in the field of approximate computing for power and energy optimization.