Reconfigurable Multi-core Architecture Research Articles

Reconfigurable multi-core array architecture and mapping method for RNS-based homomophic encryption

Fully homomorphic encryption (FHE) plays a vital role in privacy-preserving outsourcing computing and cloud computing security. However, efficiency is still the main factor limiting the actual use of FHE. This paper presents an area-efficient reconfigurable multi-core array architecture (named RMCA) and mapping method for the RNS variant of CKKS scheme. To accelerate the time-consuming polynomial multiplication, we present an improved NTT/INTT algorithm without pre/post-processing and a reconfigurable processing element (PE) unit that can be configured as NTT, INTT and modular multiplier. Also, a memory-saving NTT architecture and memory organization of the twiddle factor are introduced to reduce the data and twiddle factor memory overhead by 25 % and 50 %, respectively. Furthermore, targeting the computational requirements of RNS-CKKS, a unified computational model and distributed on-chip memory organization are presented for RMCA. Lastly, all the computational units involved in the homomorphic evaluation of RNS-CKKS are optimized and mapped on RMCA reasonably. When evaluated on Virtex UltraScale XCVU190 FPGA at 300 MHz, RMCA can perform 9154, 4308 and 1743 homomorphic multiplications per second for N = 4096, 8192 and 16384, respectively, and the area-time-products (ATPs) are improved by 1.11×∼8.60 × for a wide range of parameter sets.

A Highly Unified Reconfigurable Multicore Architecture to Speed Up NTT/INTT for Homomorphic Polynomial Multiplication

The ring learning with error (RLWE)-based fully homomorphic encryption (FHE) scheme has become one of the most promising FHE schemes. However, its performance is limited by the homomorphic multiplication, especially the polynomial multiplication which occupies major computing resources. Therefore, efficient implementation of polynomial multiplication is crucial for high-performance FHE applications. In this article, we present an area-efficient and highly unified reconfigurable multicore number theoretic transform (NTT)/inverse NTT (INTT) architecture (named MCNA), which employs NTT and INTT for polynomial multiplier with a variable number of reconfigurable processing elements. To reduce latency, MCNA merges the preprocessing and postprocessing into the constant-geometry NTT and INTT, respectively. Also, a reconfigurable modular multiplier based on digital signal processor (DSP) is proposed to speed up the modular multiplication. In order to avoid designing independent memory access pattern for INTT, a unified read/write structure of NTT/INTT is presented. Furthermore, a novel memory access pattern named “cyclic-sharing” is proposed to reduce 25% memory capacity. MCNA is evaluated on a Xilinx Virtex-7 field-programmable gate array (FPGA) platform. Running at 250-MHz clock frequency, the throughput of MCNA for NTT/INTT achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.78\times \sim 9.32\times $ </tex-math></inline-formula> improvements in comparison to prior works, while the area efficiency of lookup table (LUT) and flip-flop (FF) is improved by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.25\times \sim 4.79\times $ </tex-math></inline-formula> . For polynomial multiplication, the throughput of MCNA achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.73\times \sim 7.69\times $ </tex-math></inline-formula> enhancements, as well as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.13\times \sim 14.8\times $ </tex-math></inline-formula> area efficiency improvements.

ReMCA: A Reconfigurable Multi-Core Architecture for Full RNS Variant of BFV Homomorphic Evaluation

Fully homomorphic encryption (FHE) allows arbitrary computation on encrypted data and thus has potential in privacy-preserving computing. However, efficiency is still the bottleneck. In this paper we present an area-efficient and highly unified reconfigurable multi-core architecture (named ReMCA) for full Residue Number System (RNS) variant of Fan-Vercauteren variant of Brakerski’s scheme (RNS-BFV), which employs a variable number of reconfigurable processing elements (PEs) and RNS channels. The PE unit can be flexibly configured as NTT, INTT or modular multiplier, thereby avoiding the need of other extra computational units. To reduce the computational complexity, ReMCA merges the pre/post-processing into NTT/INTT and unifies the read/write structure of NTT and INTT. Also, a conflict-free memory access pattern that doesn’t need separate bit-reversal operation is proposed to optimize the memory access. Furthermore, targeting different computational requirements, a unified hardware architecture mapping model and data memory organization model are introduced, and all the computing units that RNS-BFV involved are optimized and mapped on ReMCA. ReMCA is evaluated on a Xilinx Virtex-7 FPGA platform. Running at 250MHz, it can perform 2260 homomorphic multiplication per second. When normalized to the same parameter set, the throughput and Area-Time-Products (ATPs) of ReMCA achieve <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.45\times \sim 5.51\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.58\times \sim 5.12\times $ </tex-math></inline-formula> improvements.

An Analysis of the Impact of Gating Techniques on the Optimization of the Energy Dissipated in Real-Time Systems

The paper concerns research on electronics-embedded safety systems. The authors focus on the optimization of the energy consumed by multitasking real-time systems. A new flexible and reconfigurable multi-core architecture based on pipeline processing is proposed. The presented solution uses thread-interleaving mechanisms that allow avoiding hazards and minimizing unpredictability. The proposed architecture is compared with the classical solutions consisting of many processors and based on the scheme using one processor per single task. Energy-efficient task mapping is analyzed and a design methodology, based on minimizing the number of active and utilized resources, is proposed. New techniques for energy optimization are proposed, mainly, clock gating and switching-resources blocking. The authors investigate two main factors of the system: setting the processing frequency, and gating techniques; the latter are used under the assumption that the system meets the requirements of time predictability. The energy consumed by the system is reduced. Theoretical considerations are verified by many experiments of the system’s implementation in an FPGA structure. The set of tasks tested consists of programs that implement Mälardalen WCET benchmark algorithms. The tested scenarios are divided into periodic and non-periodic execution schemes. The obtained results show that it is possible to reduce the dynamic energy consumed by real-time applications’ meeting their other requirements.

Predicting performance in multi-core systems with shared reconfigurable accelerators

Reconfigurable multi-core architectures, where general-purpose processors are extended with reconfigurable logic, present an interesting design choice for their capacity of efficiently executing both sequential as well as parallel workloads. Current state-of-the-art implementations distinguish between dedicated accelerators (where each core has access to its own reconfigurable fabric, for maximum performance) and shared ones (where a reconfigurable fabric is shared among cores, for maximum resource utilization), but no design-time guidance is provided for choosing the best implementation. In this work, we propose a new metric to guide the design of shared reconfigurable accelerators (independent of the hardware implementation) in multi-core environments by numerically assessing their rate of concurrency. We evaluate it with an 8-core system where any given number of coarse-grained reconfigurable arrays supporting transparent acceleration (i.e. with no changes in the binary) are available to execute multi-threaded applications. Using performance simulation from gem5, we show that while poorly correlated with an application’s thread-level parallelism (TLP), the proposed metric correlates strongly with the speedup achieved when switching from a shared fabric to dedicated per-core accelerators, and therefore can aid the designer in choosing the best strategy for implementing a reconfigurable multi-core architecture.

Best-by-Simulations: A Framework for Comparing Efficiency of Reconfigurable Multicore Architectures on Workloads with Deadlines

Energy consumption is a major concern in multicore systems. Perhaps the simplest strategy for reducing energy costs is to use only as many cores as necessary while still being able to deliver a desired quality of service. Motivated by earlier work on a dynamic (heterogeneous) core allocation scheme for H.264 video decoding that reduces energy costs while delivering desired frame rates, we formulate operationally the general problem of executing a sequence of actions on a reconfigurable machine while meeting a corresponding sequence of absolute deadlines, with the objective of reducing cost. Using a transition system framework that associates costs (e.g., time, energy) with executing an action on a particular resource configuration, we use the notion of amortised cost to formulate in terms of simulation relations appropriate notions for comparing deadline-conformant executions. We believe these notions can provide the basis for an operational theory of optimal cost executions and performance guarantees for approximate solutions, in particular relating the notion of simulation from transition systems to that of competitive analysis used for, e.g., online algorithms.

Power Mitigation by Performance Equalization in a Heterogeneous Reconfigurable Multicore Architecture

This paper presents an integrated self-aware computing model mitigating the power dissipation of a heterogeneous reconfigurable multicore architecture by dynamically scaling the operating frequency of each core. The power mitigation is achieved by equalizing the performance of all the cores for an uninterrupted exchange of data. The multicore platform consists of heterogeneous Coarse-Grained Reconfigurable Arrays (CGRAs) of application-specific sizes and a Reduced Instruction-Set Computing (RISC) core. The CGRAs and the RISC core are integrated with each other over a Network-on-Chip (NoC) of six nodes arranged in a topology of two rows and three columns. The RISC core constantly monitors and controls the performance of each CGRA accelerator by adjusting the operating frequencies unless the performance of all the CGRAs is optimally balanced over the platform. The CGRA cores on the platform are processing some of the most computationally-intensive signal processing algorithms while the RISC core establishes packet based synchronization between the cores for computation and communication. All the cores can access each other's computational and memory resources while processing the kernels simultaneously and independently of each other. Besides general-purpose processing and overall platform supervision, the RISC processor manages performance equalization among all the cores which mitigates the overall dynamic power dissipation by 20.7 % for a proof-of-concept test.

Reconfigurable multi-core architecture - a plausible solution to the von Neumann performance bottleneck

The ill-famed von Neumann bottleneck has been the main performance hurdle since the invention of computers. Although several techniques such as separate data/instruction caches, branch prediction, and parallel computing have been proposed and improved efficiency, the throughput bottleneck between CPU and memory is still very much there. We propose a novel reconfigurable multi-core architecture (RMA) to address this issue via the dynamic allocation of heterogeneous computing resources and distributed memory. We show how this is feasible with the state-of-the-art technologies of dynamic partial reconfiguration of hardware resources and runtime operating system configuration. Experiments and analysis show how RMA alleviates the performance bottleneck.

ReKonf: Dynamically reconfigurable multiCore architecture

The increased transistor count resulting from ever-decreasing feature sizes has enabled the design of architectures containing many small but efficient processing units (cores). At the same time, many new applications have evolved with varying performance requirements. The fixed architecture of multiCore platforms often fails to accommodate the inherent diverse requirements of different applications. We present a dynamically reconfigurable multiCore architecture that detects program phase change at runtime and adapts to the changing program behavior by reconfiguring itself. We introduce simple but efficient performance counters to monitor vital parameters of reconfigurable architectures. We also present static, dynamic and adaptive reconfiguration techniques for reconfiguring the architecture. Our evaluation of the proposed reconfigurable architecture using an adaptive reconfiguration technique shows an improvement of up to 23% for multi-threaded applications and up to 27% for multiprogrammed workloads over that on statically chosen architectures, and up to 41% over the baseline SMP configuration.

Energy and throughput aware fuzzy logic based reconfiguration for MPSoCs

Multicore architectures offer an amount of parallelism that is often underutilized, as a result these underutilized resources become a liability instead of advantage. Inefficient resource sharing on the chip can have a negative impact on the performance of an application and may result in greater energy consumption. A large body of research now focuses on reconfigurable multicore architectures in order to support algorithms to find optimal solutions for improved energy and throughput balance. An ideal system would be able to optimize such reconfigurable systems to a level that optimum resources are allocated to a particular workload and all the other underutilized resources remain inactive for greater energy savings. This paper presents a fuzzy logic based reconfiguration engine targeted to optimize a multicore architecture according to the workload requirements for optimum balance between power and performance of the system. The proposed fuzzy logic reconfiguration engine is designed around a 16-core SCMP architecture comprising of reconfigurable cache memories, power gated cores and adaptive on-chip network routers for minimizing leakage energy effects for inactive components. A coarse grained architecture was selected for being able to reconfigure faster, thus making it feasible to be used for runtime adaptation schemes. The presented architecture is analyzed over a set of OpenMP based parallel benchmarks and results show significant energy savings in all cases.

A reconfigurable processor architecture combining multi-core and reconfigurable processing units

It's a promising way to improve performance significantly by adding reconfigurable processing unit (RPU) to a general purpose processor. In this paper, a Reconfigurable Multi-Core (RMC) architecture combining general multi-core and reconfigurable logic is proposed. Reconfigurable logic is separated into RPUs logically, which are coupled with general purpose cores as co-processors via a full crossbar switch. An RPU Manager (RPU-M) is also designed to manage RPUs. To verify RMC, a simulation method based on the Simics and Virtex 5 FPGA is adopted, which simplifies the simulation and assures the evaluation accuracy of hardware function cores. Five workloads are selected to test RMC, including 3-DES, AES, SHA2, IDCT and JPEG_ENC. The experimental results show a 3.10 times average speedup over software implementation on the original multi-core, and the data and control communication overhead on RMC is acceptable.

Bahurupi

Computing systems have made an irreversible transition towards parallel architectures with the emergence of multi-cores. Moreover, power and thermal limits in embedded systems mandate the deployment of many simpler cores rather than a few complex cores on chip. Consumer electronic devices, on the other hand, need to support an ever-changing set of diverse applications with varying performance demands. While some applications can benefit from thread-level parallelism offered by multi-core solutions, there still exist a large number of applications with substantial amount of sequential code. The sequential programs suffer from limited exploitation of instruction-level parallelism in simple cores. We propose a reconfigurable multi-core architecture, called Bahurupi, that can successfully reconcile the conflicting demands of instruction-level and thread-level parallelism. Bahurupi can accelerate the performance of serial code by dynamically forming coalition of two or more simple cores to offer increased instruction-level parallelism. In particular, Bahurupi can efficiently merge 2-4 simple 2-way out-of-order cores to reach or even surpass the performance of more complex and power-hungry 4-way or 8-way out-of-order core. Compared to baseline 2-way core, quad-core Bahurupi achieves up to 5.61 speedup (average 4.08 speedup) for embedded workloads. On an average, quad-core Bahurupi achieves 17% performance improvement and 43% improvement in energy consumption compared to 8-way out-of-order baseline core on a diverse set of embedded benchmark applications.

Reconfigurable Homogenous Multi-Core FFT Processor Architectures for Hybrid SISO/MIMO OFDM Wireless Communications

Multi-core processors have been attracting a great deal of attention. In the domain of signal processing for communications, the current trends toward rapidly evolving standards and formats, and toward algorithms adaptive to dynamic factors in the environment, require programmable solutions that possess both algorithm flexibility and low implementation complexity. Reconfigurable architectures have demonstrated better tradeoffs between algorithm flexibility, implementation complexity, and energy efficiency. This paper presents a reconfigurable homogeneous memory-based FFT processor (MBFFT) architecture integrated in a single chip to provide hybrid SISO/MIMO OFDM wireless communication systems. For example, a reconfigurable MBFFT processor with eight processing elements (PEs) can be configured for one DVB-T/H with N=8192 and two 802.11n with N=128. The reconfigurable processors can perfectly fit the applications of Software Defined Radio (SDR) which requires more hardware flexibility.