Tag: technology

FCCM 2025

I’ve recently returned from the IEEE International Symposium on Field-Programmable Custom Computing Machines (known as FCCM). I used to attend FCCM regularly in the early 2000s, and while I have continued to publish there, I have not attended myself for some years. I tried a couple of years ago, but ended up isolated with COVID in Los Angeles. In contrast, I am pleased to report that the conference is in good health!

The conference kicked off on the the evening of the 4th May, with a panel discussion on the topic of “The Future of FCCMs Beyond Moore’s Law”, of which I was invited be be part, alongside industrial colleagues Chris Lavin and Madhura Purnaprajna from AMD, Martin Langhammer from Altera, and Mark Shand from Waymo. Many companies have tried and failed to produce lasting post-Moore alternatives to the FPGA and the microprocessor over the decades I’ve been in the field and some of these ideas and architectures (less commonly, associated compiler flows / design tools) have been very good. But, as Keynes said, “markets can remain irrational longer than you can remain solvent”. So instead of focusing on commercial realities, I tried to steer the panel discussion towards the genuinely fantastic opportunities our academic field has for a future in which power, performance and area innovation changes become a matter of intellectual advances in architecture and compiler technology rather than riding the wave of technology miniaturisation (itself, of course, the product of great advances by others).

The evening panel, as imagined by AI. I’m 2nd to left. The AI tool was clearly unaware of Martin’s height difference!
The reality of the panel.
We look older, and we don’t have beer.

The following day, the conference proper kicked off. Some highlights for me from other authors included the following papers aligned with my general interests:

  • AutoNTT: Automatic Architecture Design and Exploration for Number Theoretic Transform Acceleration on FPGAs from Simon Fraser University, presented by Zhenman Fang.
  • RealProbe: An Automated and Lightweight Performance Profiler for In-FPGA Execution of High-Level Synthesis Designs from Georgia Tech, presented by Jiho Kim from Callie Hao‘s group.
  • High Throughput Matrix Transposition on HBM-Enabled FPGAs from the University of Southern California (Viktor Prasanna‘s group).
  • ITERA-LLM: Boosting Sub-8-Bit Large Language Model Inference Through Iterative Tensor Decomposition from my colleague Christos Bouganis‘ group at Imperial College, presented by Keran Zheng.
  • Guaranteed Yet Hard to Find: Uncovering FPGA Routing Convergence Paradox from Mirjana Stojilovic‘s group at EPFL – and winner of this year’s best paper prize!

In addition, my own group had two full papers at FCCM this year:

  • Banked Memories for Soft SIMT Processors, joint work between Martin Langhammer (Altera) and me, where Martin has been able to augment his ultra-high-frequency soft-processor with various useful memory structures. This is probably the last paper of Martin’s PhD – he’s done great work in both developing a super-efficient soft-processor and in forcing the FPGA community to recognise that some published clock frequency results are really quite poor and that people should spend a lot longer thinking about the physical aspects of their designs if they want to get high performance.
  • NeuraLUT-Assemble: Hardware-aware Assembling of Sub-Neural Networks for Efficient LUT Inference, joint work between my PhD student Marta Andronic and me. I think this is a landmark paper in terms of the results that Marta has been able to achieve. Compared to her earlier NeuraLUT work which I’ve blogged on previously, she has added a way to break down large LUTs into trees of smaller LUTs, and a hardware-aware way to learn sparsity patterns that work best, localising nonlinear interactions in these neural networks to within lookup tables. The impact of these changes on the area and delay of her designs is truly impressive.
Martin explaining efficient
memory structures for soft processors
Marta explaining LUT-based neural networks on FPGAs

Overall, it was well worth attending. Next year, Callie will be hosting FCCM in Atlanta.

Energy: Rewriting the Possibilities

In early June, my PhD student Sam Coward (co-advised by Theo Drane from Intel), will travel to ARITH 2024 in Málaga to present some of our most recent work, “Combining Power and Arithmetic Optimization via Datapath Rewriting”, a joint paper with Emiliano Morini, also of Intel. In this blog post, I will describe the fundamental idea of our work.

It’s well-known that ICT is driving a significant amount of energy consumption in the modern world. The core question of how to organise the fundamental arithmetic operations in a computer in order to reduce power (energy per unit time) has been studied for a long time, and continues to be a priority for designers across industry, including the group at Intel with whom this work has been conducted.

Readers of this blog will know that Sam has been doing great work on how to explore the space of behaviourally equivalent hardware designs automatically. First for area, then for performance, and now for power consumption!

In our latest work, Sam looks at how we can use the e-graph data structure, and the related egg tool, to tightly integrate arithmetic optimisations (like building multi-input adders in hardware) with clock gating and data gating, two techniques for power saving. Clock gating avoids clocking new values into registers in hardware if we know they’re not going to be used in a given cycle; this avoids the costly switching activity associated with propagating unused information in a digital circuit. Data gating also avoids switching, but in a different way – by replacing operands with values inducing low switching: for example, if I do not end up using a result of a \times b, then I may as well be computing a \times 0. In both cases, the fundamental issue becomes how to identify whether a value will be unused later in a computation. Intriguingly, this question is tightly related to the way a computation is performed: there are many ways of computing a given mathematical computation, and each one will have its own redundancies to exploit.

In our ARITH 2024 paper, Sam has shown how data gating and clock gating can be expressed as rewrites over streams of Boolean data types, lifting our previous work that looks at equivalences between bit vectors, to equivalences over streams of bit vectors. In this way, he’s able to express both traditional arithmetic equivalences like a + (b + c) = (a + b) + c and equivalences expressing clock and data gating within the same rewriting framework. A collection of these latter equivalences are shown in the table below from our paper.

Some of the rewrites between equivalent expressions used in our ARITH 2024 paper

Sam has been able to show that by combining the rewrites creatively, using arithmetic rewrites to expose new opportunity for gating, our tool ROVER is able to save some 15% to 30% of power consumption over a range of benchmark problems of industrial interest. Moreover, ROVER will automatically adjust the whole design to better suit different switching profiles, knowing that rarely-switching circuit components are less problematic for energy, and prioritising exposing rewrites where they are needed.

I think this is really interesting work, and shows just how general the e-graph approach to circuit optimisation can be. If you’re going to ARITH 2024, do make sure to talk to Sam and find out more. If not, make sure to read his paper!