Compute Express Link (CXL) is an interconnect that extends the traditional memory hierarchy beyond a single machine. It enables disaggregated memory architecture, where memory can be pooled and shared across multiple hosts effectively. Previous systems and approaches were limited to CXL 1.0 which is restricted to intra-host scaling of memory resources with no out of the box support for sharing across multiple hosts. We evaluate a commercially available CXL 2.0 memory switching platform for multi-host shared memory. While prior work has largely focused on emulation, FPGA-based or non-available prototypes, we investigate the real-world performance and system implications of deploying CXL as shared memory across multiple hosts on a commercially available setup. Our evaluation reveals fundamental trade-offs: Access to shared CXL memory exhibits up to 4x higher latency and significantly lower bandwidth compared to local DRAM, but enables flexible memory pooling with fair sharing across hosts. More importantly, we show that the lack of hardware cache coherence in CXL 2.0 introduces non-trivial challenges, requiring carefully chosen instruction patterns.

Digitizing side-channel signals at high sampling rates produces huge amounts of data, while side-channel analysis techniques only need those specific trace segments containing Cryptographic Operations (COs). For detecting these segments, waveform-matching techniques have been established comparing the signal with a template of the CO’s characteristic pattern. Real-time waveform matching requires highly parallel implementations as achieved by hardware design but also reconfigurability as provided by Field-Programmable Gate Arrays (FPGAs) to adapt the matching hardware to a specific CO pattern. However, currently proposed designs process the samples from analog-to-digital converters sequentially and can only process low sampling rates due to the limited clock speed of FPGAs. In this article, we present a parallel waveform-matching architecture capable of performing high-speed waveform matching on a high-end FPGA-based digitizer. We also present a workflow for calibrating the waveform-matching system to the specific pattern of the CO in the presence of hardware restrictions provided by the FPGA hardware. Our implementation enables waveform matching at 10 GS/s, offering a speedup of 50× compared to the fastest state-of-the-art implementation known to us. We demonstrate how to apply the technique for attacking the widespread XTS-AES algorithm using waveform matching to recover the encrypted tweak even in the presence of so-called systemic noise.

Many Big Data applications include the processing of data streams on semi-structured data formats such as JSON. A disadvantage of such formats is that an application may spend a significant amount of processing time just on unselectively parsing all data. To relax this issue, the concept of raw filtering is proposed with the idea to remove data from a stream prior to the costly parsing stage. However, as accurate filtering of raw data is often only possible after the data has been parsed, raw filters are designed to be approximate in the sense of allowing false-positives in order to be implemented efficiently. Contrary to previously proposed CPU-based raw filtering techniques that are restricted to string matching, we present FPGA-based primitives for filtering strings, numbers and also number ranges. In addition, a primitive respecting the basic structure of JSON data is proposed that can be used to further increase the accuracy of introduced raw filters. The proposed raw filter primitives are designed to allow for their composition according to a given filter expression of a query. Thus, complex raw filters can be created for FPGAs which enable a drastical decrease in the amount of generated false-positives, particularly for IoT workload. As there exists a trade-off between accuracy and resource consumption, we evaluate primitives as well as composed raw filters using different queries from the RiotBench benchmark. Our results show that up to 94.3% of the raw data can be filtered without producing any observed false-positives using only a few hundred LUTs.

Side-Channel Analysis (SCA) requires the detection of the specific time frame within which Cryptographic Operations (COs) take place in the side-channel signal. In laboratory conditions with full control over the Device under Test (DuT), dedicated trigger signals can be implemented to indicate the start and end of COs. For real-world scenarios, waveform-matching techniques have been established which compare the side-channel signal with a template of the CO's pattern in real time to detect the CO in the side channel. State-of-the-art approaches are implemented on Field-Programmable Gate Arrays (FPGAs). However, current waveform-matching designs process the samples from Analog-to-Digital Converters (ADCs) sequentially and can only work with low sampling rates due to the limited clock speed of FPGAs. This makes it increasingly difficult to apply existing techniques on modern DuTs that operate with clock speeds in the GHz range. In this paper, we present a parallel waveform-matching architecture that is capable of performing waveform matching at the speed of fast ADCs. We implement the proposed architecture in a high-end FPGA-based digitizer and deploy it to detect AES COs from the side channel of a single-board computer operating at 1 GHz. Our implementation allows for waveform matching at 10 GS/s with high accuracy, thus offering a speedup of 50× compared to the fastest state-of-the-art implementation known to us.

FPGA-based Physical Unclonable Functions (PUF) have emerged as a viable alternative to permanent key storage by turning effects of inaccuracies during the manufacturing process of a chip into a unique, FPGA-intrinsic secret. However, many fixed PUF designs may suffer from unsatisfactory statistical properties in terms of uniqueness, uniformity, and robustness. Moreover, a PUF signature may alter over time due to aging or changing operating conditions, rendering a PUF insecure in the worst case. As a remedy, we propose CHOICE , a novel class of FPGA-based PUF designs with tunable uniqueness and reliability characteristics. By the use of addressable shift registers available on an FPGA, we show that a wide configuration space for adjusting a device-specific PUF response is obtained without any sacrifice of randomness. In particular, we demonstrate the concept of address-tunable propagation delays, whereby we are able to increase or decrease the probability of obtaining “ 1 ”s in the PUF response. Experimental evaluations on a group of six 28 nm Xilinx Artix-7 FPGAs show that CHOICE PUFs provide a large range of configurations to allow a fine-tuning to an average uniqueness between 49% and 51%, while simultaneously achieving bit error rates below 1.5%, thus outperforming state-of-the-art PUF designs. Moreover, with only a single FPGA slice per PUF bit, CHOICE is one of the smallest PUF designs currently available for FPGAs. It is well-known that signal propagation delays are affected by temperature, as the operating temperature impacts the internal currents of transistors that ultimately make up the circuit. We therefore comprehensively investigate how temperature variations affect the PUF response and demonstrate how the tunability of CHOICE enables us to determine configurations that show a high robustness to such variations. As a case study, we present a cryptographic key generation scheme based on CHOICE PUF responses as device-intrinsic secret and investigate the design objectives resource costs, performance, and temperature robustness to show the practicability of our approach.

FPGA-based Physical Unclonable Functions (PUFs) have emerged as a viable alternative to permanent key storage by turning inaccuracies during the manufacturing process of a chip into a unique, FPGA-intrinsic secret. However, many fixed PUF designs may suffer from unsatisfactory statistical properties in terms of uniqueness, uniformity, and robustness. Moreover, a PUF signature may alter over time due to aging or changing operating conditions, rendering a PUF insecure in the worst case. As a remedy, we propose CHOICE, a novel class of FPGA-based PUF designs with tunable uniqueness and reliability characteristics. By the use of addressable shift registers available on an FPGA, we show that a wide configuration space for adjusting a device-specific PUF response is obtained without any sacrifice of randomness. In particular, we demonstrate the concept of address-tunable propagation delays, whereby we are able to increase or decrease the probability of obtaining 1’s in the PUF response. Experimental evaluations on a group of six 28 nm Xilinx Artix-7 FPGAs show that CHOICE PUFs provide a large range of configurations to allow a fine-tuning to an average uniqueness between 49% and 51%, while simultaneously achieving bit error rates below 1.5%, thus outperforming state-of-the-art PUF designs. Moreover, with only a single FPGA slice per PUF bit, CHOICE is one of the smallest PUF designs currently available for FPGAs.

Hardware acceleration of database query processing can be done with the help of FPGAs. In particular, they are partially reconfigurable at runtime, which allows for the adaptation to a variety of queries. Reconfiguration itself, however, takes some time. This paper presents optimizations based on query sequences, which reduce the impact of the reconfigurations. Knowledge of upcoming queries is used to avoid reconfiguration overhead. We evaluate our optimizations with a calibrated model. Improvements in execution time of up to 28% can be obtained even with sequences of only two queries.

Hardware Accelerators for Query Processing are often optimized to filter as much data as possible before the results are stored to memory or sent to the user. However, the static nature of such optimized accelerators limits the amount of operators they can implement to a fixed amount configured at synthesis time. If not all operators can be pushed down to the accelerator, the decision which operators should be pushed down has therefore a big impact on the resulting data size and overall query execution time. Statistics are therefore used to determine which operators to push onto the hardware accelerators. Gathering these statistics is therefore of utmost importance. In this paper we present multiple possibilities to gather statistics within an accelerator while executing a partial query. These statistics can be gradually improved with every execution of the accelerator be of use in future queries during the query planning phase.

Regular expressions (regex) are the main means to search for specific patterns in the vast amount of stored textual information. As a consequence, different designs of hardware accelerators have been proposed that enable memory-bound regex processing. Here, the regular expression to be evaluated is translated to a non-deterministic (NFA) or deterministic finite automaton (DFA) which is then mapped onto the hardware design. The available hardware resources of the design imply the maximum size (in terms of amount of states and transitions) of the supported automata. However, regular expressions may be arbitrarily complex.

Approximate Computing aims at trading off computational accuracy against improvements regarding performance, resource utilization and power consumption by making use of the capability of many applications to tolerate a certain loss of quality. A key issue is the dependency of the impact of approximation on the input data as well as user preferences and environmental conditions. In this context, we therefore investigate the concept of self-adaptive image processing that is able to autonomously adapt 2D-convolution filter operators of different accuracy degrees by means of partial reconfiguration on Field-Programmable-Gate-Arrays (FPGAs). Experimental evaluation shows that the dynamic system is able to better exploit a given error tolerance than any static approximation technique due to its responsiveness to changes in input data. Additionally, it provides a user control knob to select the desired output quality via the metric threshold at runtime.

In this paper, we propose a novel approximate adder structure for LUT-based FPGA technology. Compared with a full featured accurate carry-ripple adder, the longest path is significantly shortened which enables the clocking with an increased clock frequency. By using the proposed adder structure, the throughput of an FPGA-based implementation can be significantly increased. On the other hand, the resulting average error can be reduced compared to similar approaches for ASIC implementations.

During the design of embedded systems, many design decisions have to be made to trade off between conflicting objectives such as cost, performance, and power. Approximate computing allows to optimize each objective, yet for the sake of accuracy. This means that a functional flaw is allowed to produce an error as long as this is small enough to maintain a feasible operation of the system or guarantee a certain accuracy of the results. In this paper, we propose a new technique for approximate addition optimized for LUT-Based FPGAs with segmented carry chains. Our optimized adder structure is able to a) best exploit artifacts of LUT-Based FPGAs such as unused inputs and b) provide a smaller average error than previously proposed approximate adder structures, as well as c) a reduced critical path delay than dedicated accurate logic in modern FPGAs. We present a novel stochastic error calculus that is able to take into account also non-uniform input distributions and present a detailed comparison of approximate adder structures proposed in literature with our novel LUT-Based approximate arithmetic structure.

In this article, we propose an FPGA-based SQL query processing approach exploiting the capabilities of partial dynamic reconfiguration of modern FPGAs. After the analysis of an incoming query, a query-specific hardware processing unit is generated on the fly and loaded on the FPGA for immediate query execution. For each query, a specialized hardware accelerator pipeline is composed and configured on the FPGA from a set of presynthesized hardware modules. These partially reconfigurable hardware modules are gathered in a library covering all major SQL operations like restrictions and aggregations, as well as more complex operations such as joins and sorts. Moreover, this holistic query processing approach in hardware supports different data processing strategies including row- as column-wise data processing in order to optimize data communication and processing. This article gives an overview of the proposed query processing methodology and the corresponding library of modules. Additionally, a performance analysis is introduced that is able to estimate the processing time of a query for different processing strategies and different communication and processing architecture configurations. With the help of this performance analysis, architectural bottlenecks may be exposed and future optimized architectures, besides the two prototypes presented here, may be determined.

In this paper, we present a novel co-designed architecture for high throughput database query processing. It consists of a highly configurable FPGA-based filter chain with arithmetic operation support and an alignment unit. This feeds the filtered data directly and in a cache-optimized way to embedded processors which are responsible for joining tables and post processing. High throughput interfaces and parallelism of FPGA implementations are thus combined in order to provide reduced and cache-aligned data for optimized processor access. As a key component, we introduce a new highly configurable bloom filter cascade to relieve a processor of time-consuming hash-value computation and to significantly reduce the data for hash joins. It is shown that this unique approach may reduce the amount of data to be processed by the processors in typical data-warehouse applications by several orders of magnitude. The proposed co-design has been implemented on the embedded low-energy system-on-chip (SoC) platform Xilinx Zynq. Performance results for standard benchmarks show an up to 10 x higher throughput compared to a full featured x86-based processor at only a fraction of energy consumption.

In this paper, we propose an approach for energy-aware FPGA-based query acceleration for databases on embedded devices. After the analysis of an incoming query, a query-specific hardware accelerator is generated on-the-fly and loaded on the FPGA for subsequent query execution using partial dynamic reconfiguration. For each SQL query operation, a pre-synthesized partial bitstream implementation exists in a module library. This library includes modules for all major SQL operations like restrictions, aggregations, as well as more complex operations such as join and sort. The implementation of this flexible FPGA-based query accelerator approach on the embedded low-energy system-on-chip (SoC) platform Xilinx Zynq shows SQL query processing speeds comparable to high-end database servers, however, at a much lower energy consumption. Indeed, provided experimental results give evidence that the proposed architecture may reduce the amount of consumed energy to just 5% of the energy needed of an in-memory database system running on an x86-based server at equal throughput for respective benchmarks.