With network requirements diverging across emerging applications, latency-critical services demand minimal logic delay, while hyperscale training and collectives require sustained line-rate throughput for synchronized bulk transfers. This divergence creates an urgent need for custom network switches tailored to specialized protocols and application-specific traffic patterns. This paper presents SPAC (Switch and Protocol Adaptive Customization), a novel approach that automates the generation of FPGA-based network switches co-optimized for custom protocols and application-specific traffic patterns. SPAC introduces a unified workflow with a domain-specific language (DSL) for protocol-architecture co-design, a library of modular HLS-based adaptive switch components, and a trace-aware Design Space Exploration (DSE) engine. By providing a multi-fidelity simulation stack, SPAC enables rapid identification of Pareto-optimal designs prior to deployment. We demonstrate the efficacy of the domain-specific adaptation of SPAC across a spectrum of real-world scenarios, spanning from latency-sensitive sensor and HFT networks to hyperscale datacenter fabrics. Experimental results show that by tailoring the micro-architecture and protocol to the specific workload, SPAC-generated designs reduce LUT and BRAM usage by 55% and 53%, respectively. Compared to fixed-architecture counterparts, SPAC delivers latency reductions ranging from 7.8% to 38.4% across various tasks while maintaining adequate resource consumption and packet drop rate.1

Aggregation queries are a series of computationally-demanding analytics operations on grouped or time series data. Existing challenges include the increased hardware utilisation and random memory access patterns that result from hash-based approaches or multi-tasking. This paper presents a high-throughput and reconfigurable pipeline for a wide range of aggregation tasks with a minimal hardware overhead.

Network-on-chips (NoCs) are currently a widely used approach for achieving scalability of multi-cores to many-cores, as well as for interconnecting other vital system-on-chip (SoC) components. Each entity in 2D mesh-based NoCs has a router responsible for forwarding packets between the dimensions as well as the entity itself, and it is essentially a 5-port switch. With respect to the routing algorithm, there are important trade-offs between routing performance and the efficiency of overcoming potential deadlocks. Common deadlock avoidance techniques including the turn model usually involve restrictions of certain paths a packet can take at the cost of a higher probability for network congestion. In contrast, deadlock resolution techniques, as well as some avoidance schemes, provide more path flexibility at the expense of hardware complexity, such as by incorporating (or assuming) dedicated buffers. This paper provides a deadlock avoidance algorithm for NoC routers based on output-queues (OQs) or virtual-output queues (VOQs), with a focus on their use on field-programmable gate-arrays (FPGAs). The proposed approach features fewer path restrictions than common techniques, and can be based on existing routing algorithms as a baseline, deadlock-free or not. This requires no modification to the queueing topology, and the required logic is minimal. Our algorithm approaches the performance of fully-adaptive algorithms, while maintaining deadlock freedom.

This article studies small to medium-sized monolithic switches for FPGA implementation and presents a novel switch design that achieves high algorithmic performance and FPGA implementation efficiency. Crossbar switches based on virtual output queues (VOQs) and variations have been rather popular for implementing switches on FPGAs, with applications in network switches, memory interconnects, network-on-chip (NoC) routers etc. The implementation efficiency of crossbar-based switches is well-documented on ASICs, though we show that their disadvantages can outweigh their advantages on FPGAs. One of the most important challenges in such input-queued switches is the requirement for iterative scheduling algorithms. In contrast to ASICs, this is more harmful on FPGAs, as the reduced operating frequency and narrower packets cannot “hide” multiple iterations of scheduling that are required to achieve a modest scheduling performance. Our proposed design uses an output-queued switch internally for simplifying scheduling, and a queue balancing technique to avoid queue fragmentation and reduce the need for memory-sharing VOQs. Its implementation approaches the scheduling performance of a state-of-the-art FPGA-based switch, while requiring considerably fewer resources.

In this paper, we present FLiMS, a highly-efficient and simple parallel algorithm for merging two sorted lists residing in banked and/or wide memory. On FPGAs, its implementation uses fewer hardware resources than the state-of-the-art alternatives, due to the reduced number of comparators and elimination of redundant logic found on prior attempts. In combination with the distributed nature of the selector stage, a higher performance is achieved for the same amount of parallelism or higher. This is useful in many applications such as in parallel merge trees to achieve high-throughput sorting, where the resource utilisation of the merger is critical for building larger trees and internalising the workload for faster computation. Also presented are efficient variations of FLiMS for optimizing throughput for skewed datasets, achieving stable sorting or using fewer dequeue signals. FLiMS is also shown to perform well as conventional software on modern CPUs supporting single-instruction multiple-data (SIMD) instructions, surpassing the performance of some standard libraries for sorting.

We present Hipernetch, a novel FPGA-based design for performing high-bandwidth network switching. FPGAs have recently become more popular in data centers due to their promising capabilities for a wide range of applications. With the recent surge in transceiver bandwidth, they could further benefit the implementation and refinement of network switches used in data centers. Hipernetch replaces the crossbar with a “combined parallel round-robin arbiter”. Unlike a crossbar, the combined parallel round-robin arbiter is easy to pipeline, and does not require centralised iterative scheduling algorithms that try to fit too many steps in a single or a few FPGA cycles. The result is a network switch implementation on FPGAs operating at a high frequency and with a low port-to-port latency. Our proposed Hipernetch architecture additionally provides a competitive switching performance approaching output-queued crossbar switches. Our implemented Hipernetch designs exhibit a throughput that exceeds 100 Gbps per port for switches of up to 16 ports, reaching an aggregate throughput of around 1.7 Tbps.

This demo elaborates on the programmability aspect of Simodense, a recently released open-source softcore, optimised for evaluating custom SIMD instructions. CPUs featuring small reconfigurable areas for implementing custom instructions is an alternative path in computer architecture that can help with the challenges found in today’s FPGAs. By providing RTL-based programmability for implementing custom SIMD instructions, highly-integrated accelerators can be developed, while benefiting from the pre-existing CPU logic, such as the caches and their high memory throughput to main memory.

Simodense is a high-performance open-source RISC-V (RV32IM) softcore, optimised for exploring custom SIMD instructions. In order to maximise SIMD instruction performance, the design’s memory system is optimised for streaming bandwidth, such as very wide blocks for the last-level cache. The approach is demonstrated on example memory-intensive applications with custom instructions. This paper also provides insights on the effectiveness of adding FPGA resources in general purpose processors in the form of reconfigurable SIMD instructions.

This paper presents a novel FPGA-based switch design that achieves high algorithmic performance and an efficient FPGA implementation. Crossbar switches based on virtual output queues (VOQs) and variations have been rather popular for implementing switches on FPGAs, with applications to network-on-chip (NoC) routers and network switches. The efficiency of VOQs is well-documented on ASICs, though we show that their disadvantages can outweigh their advantages on FPGAs. Our proposed design uses an output-queued switch internally for simplifying scheduling, and a queue balancing technique to avoid queue fragmentation and reduce the need for memory-sharing VOQs. Our implementation approaches the scheduling performance of the state-of-the-art, while requiring considerably fewer FPGA resources.

We present a high-throughput FPGA design for supporting high-performance network switching. FPGAs have recently been attracting attention for datacenter computing due to their increasing transceiver count and capabilities, which also benefit the implementation and refinement of network switches. Our solution replaces the crossbar in favour of a novel, more pipeline-friendly approach, the "Combined parallel round-robin arbiter". It also removes the overhead of incorporating an often-iterative scheduling or matching algorithm, which sometimes tries to fit too many steps in a single or a few FPGA cycles. The result is a network switch implementation on FPGAs operating at a high frequency and with a low port-to-port latency. It also provides a wiser buffer memory utilisation than traditional Virtual Output Queue (VOQ)-based switches and is able to keep 100% throughput for a wider range of traffic patterns using a fraction of the buffer memory and shorter packets.

This work improves on the latest research about sorting acceleration on FPGAs. An efficient design is introduced for sorting data that fit on-chip, with the additional functionality to merge sorted sublists recursively, for an input of arbitrary length. While many-leaf mergers are conventionally single-rate, a novel technique in our approach is to use a parallel merge tree only for the latest stages of the merge tree, to enable bandwidth-adapted multi-rate many-leaf merge. Our open-source RTL generator produces sorting peripherals with customisable parallelism and data format. We evaluate our FPGA design as an 128-bit wide peripheral on an MPSoC platform, with a speedup of up to 49 times over the A53 core for sorting, and up to 27 times speedup for our specialized database analytics application.

We present an efficient, high-throughput and scalable hardware design for accelerating the merge phase of the sort-merge join operation. Sort-merge join is one of the fundamental join algorithms and among the most frequently executed operations in relational databases. It has been the focus of various recent pieces of research, each having different shortcomings and usually only focusing on the sort phase. In this paper, a new parallel sort-merge join architecture is developed, that provides data streaming functionality and high throughput. The key idea of the paper is the use of a novel design for a co-grouping engine, with which the input data are summarised on-the-fly. In this way, the operation is performed on streams of data, preserving the linear access pattern for faster data movement and also eliminates the need for a replay buffer/cache. In contrast to related work, our approach does not make assumptions about the value distribution of the input data and applies to any input size and width. We evaluate the design on a Zynq UltraScale+ based platform, and show that there is up to 3.1 times speedup over the host processor, even without accelerating the sort phase, while still taking into account the data transfers from and to main memory in Linux.

Database systems are key to a variety of applications, and FPGA-based accelerators have shown promise in supporting high-performance database systems. This survey presents a systematic review of research relating to accelerating analytical database systems using FPGAs. The review includes studies of database acceleration frameworks and accelerator implementations for various database operators. Finally, the survey includes some promising future technologies and discussion on the challenges to be addressed by the future research in this area.

We have developed a highly-efficient and simple parallel hardware design for merging two sorted lists residing in banked (or multi-ported) memory. The FPGA implementation uses half the hardware resources required for implementing the current state-of-the-art architecture. This is achieved with better performance and half the latency, for the same amount of parallelism. The challenges for the merge operations in FPGAs have been the low clock frequency due to the feedback datapath of the merger being the critical path for timing, and also the high resource utilisation in recent attempts to eliminate/remove the feedback datapath. Our solution uses a modified version of the bitonic merge block, as found in a bitonic sorter, repurposed for performing parallel merge for streaming data. As with the state-of-the-art, it can be considered feedback-less since it only nests one parallel comparison for any desired level of parallelism. This leads to high operating frequency designs, 1.3 times higher than the previous best on our test platform. Since the new design uses 2 times fewer hardware resources, it allows more parallelism and leaves room for additional logic.