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 $2.78\times \sim 9.32\times $ improvements in comparison to prior works, while the area efficiency of lookup table (LUT) and flip-flop (FF) is improved by $1.25\times \sim 4.79\times $ . For polynomial multiplication, the throughput of MCNA achieves $3.73\times \sim 7.69\times $ enhancements, as well as $1.13\times \sim 14.8\times $ area efficiency improvements.

Fully homomorphic encryption (FHE) allows arbitrary computation on encrypted data and has great potential in privacy-preserving cloud computing and securely outsource computational tasks. However, the excessive computation complexity is the key limitation that restricting the practical application of FHE. In this paper we proposed a FPGA-based high parallelism architecture to accelerate the FHE schemes based on the ring learning with errors (RLWE) problem, specifically, we presented a fast implementation of leveled fully homomorphic encryption scheme BGV. In order to reduce the computation latency and improve the performance, we applied both circuit-level and block-level pipeline strategies to improve clock frequency, and as a result, enhance the processing speed of polynomial multipliers and homomorphic evaluation functions. At the same time, multiple polynomial multipliers and modular reduction units were deployed in parallel to further improve the hardware performance. Finally, we implemented and tested our architecture on a Virtex UltraScale FPGA platform. Runing at 150MHz, our implementation achieved 4.60x~9.49x speedup with respect to the optimized software implementation on Intel i7 processor running at 3.1GHz for homomorphic encryption and decryption, and the throughput was increased by 1.03x~4.64x compared to the hardware implementation of BGV. While compared to the hardware implementation of FV, the throughput of our accelerator also achieved 5.05x and 167.3x speedup for homomorphic addition and homomorphic multiplication operation respectively.

To reduce multiplication operations in convolution of convolutional neural networks (CNNs), there are three widely used convolutional acceleration algorithms, i.e., Winograd, FFT and FFA. However, current accelerators based on these convolutional acceleration algorithms have issues on flexibility and efficiency. Firstly, some accelerators utilized a combination of these acceleration algorithms and employed multiple types of computational units to achieve their respective advantages. As a result, some computational units are left unused when the best-performing unit is working, which causes much area inefficiency. Secondly, current accelerators tend to choose small parameters of these convolutional acceleration algorithms to avoid unacceptable precision loss, as a result, they are hardly to support large kernel sizes and lack of flexibility. Thirdly, these acceleration algorithms are typically presented for 1-stride convolutions, consequently, few implementation considers the acceleration of large-stride convolutions, which is a major restriction to hardware flexibility. This paper proposed a stride-based convolution decomposition method (SCDM) to reform different convolution shapes (i.e., kernel sizes & strides) to an identical pattern. With the aid of SCDM, a Winograd-stretched and hardware-efficient design (WHD) is presented to utilize one uniform computational unit for the acceleration of different convolution shapes, which combines complementary performance advantages on both Winograd F(4,3) and F(4,2) units. Compared to current FFT-based or FFA-based works, WHD can stretch the use range of Winograd and simplify implementation, thereby achieving hardware flexibility and efficiency. Evaluation results show that 34.08%~55.41% operation reduction were achieved on six CNN models, while incurring a slight hardware overhead.

As convolutional neural networks (CNNs) become more and more diverse and complicated, acceleration of CNNs increasingly encounters a bottleneck of balancing performance, energy efficiency, and flexibility in a unified architecture. This paper proposed a Winograd-based highly efficient and dynamically Reconfigurable Accelerator (named WRA) for quickly evolving CNN models. A cost-effective convolution decomposition method (CDW) was proposed, and it extends the application of the fast Winograd algorithm. Based on CDW, a high-throughput and reconfigurable processing element (PE) array was designed to exploit the parallelism of Winograd. Besides, a highly compact memory structure employed four levels of data reuse schemes to achieve maximal data reuse and minimize external bandwidth requirement. Provided with dynamically reconfigurable capability, WRA implements CDW and other convolutions (e.g., standard convolution, depthwise separable convolution, and group convolution) on a unified hardware architecture. The WRA accelerator was implemented on a Xilinx XCVU9P platform running at 330 MHz clock frequency, controlled by a POWER8 processor via a coherent accelerator processor interface (CAPI) interface. At different configurations, WRA can provide 2.2–6.3 TOPS performance for different convolution shapes. The average performance and energy efficiency for VGG16/AlexNet/MobileNetV1/MobileNetV2 are 5288 GOP/s at 151.2 GOPs/W, 3478 GOP/s at 99.4 GOPs/W, 2674 GOP/s at 76.4 GOPs/W, and 2194 GOP/s at 62.7 GOPs/W. It achieves $1.7\times $ – $24\times $ speedup compared with the previous FPGA-based designs.

In this brief, a hybrid-grained reconfigurable architecture (HReA) is introduced to process 13-Dwarfs. The proposed dynamically reconfigurable fabric consists of four 4 × 4 multi-functional processing elements array, where a hybrid-grained structure is proposed to combine a 32-bit data path with a 1-bit data path to accommodate multiple computing granularities in 13-Dwarfs. Aiming at the flexibility of the 13-Dwarfs calculation, a directional broadcasting scheme for multi-bank memory, a cache partitioning mechanism, and cache prefetching methods are proposed to further improve HReA performance via alleviating data and configuration bandwidth bottlenecks. The HReA is implemented on a 4.83 × 4.93 mm ² silicon with TSMC 65-nm LP1P8M CMOS technology and can efficiently perform 27 representative kernels from the 13-Dwarfs with 297 mW under 280-MHz working frequency.

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 $1.45\times \sim 5.51\times $ and $1.58\times \sim 5.12\times $ improvements.