ACM Transactions on Reconfigurable Technology and Systems最新文献

Elliptic Curve Cryptography (ECC), one of the most widely used asymmetric cryptographic algorithms, has been deployed in Transport Layer Security (TLS) protocol, blockchain, secure multiparty computation, etc. As one of the most secure ECC curves, Curve25519 is employed by some secure protocols, such as TLS 1.3 and Diffie-Hellman Private Set Intersection (DH-PSI) protocol. High performance implementation of ECC is required, especially for the DH-PSI protocol used in privacy-preserving platform.

Point multiplication, the chief cryptographic primitive in ECC, is computationally expensive. To improve the performance of DH-PSI protocol, we propose Topgun, a novel and high-performance hardware architecture for point multiplication over Curve25519. The proposed architecture features a pipelined Finite-field Arithmetic Unit and a simple and highly efficient instruction set architecture. Compared to the best existing work on Xilinx Zynq 7000 series FPGA, our implementation with one Processing Element can achieve 3.14 × speedup on the same device. To the best of our knowledge, our implementation appears to be the fastest among the state-of-the-art works. We also have implemented our architecture consisting of 4 Compute Groups, each with 16 PEs, on an Intel Agilex AGF027 FPGA. The measured performance of 4.48 Mops/s is achieved at the cost of 86 Watts power, which is the record-setting performance for point multiplication over Curve25519 on FPGAs.

Deterministic and Non-deterministic Finite Automata (DFA and NFA) comprise the core of many big data applications. Recent efforts to develop Domain-Specific Architectures (DSAs) for DFA/NFA have taken divergent approaches, but achieving consistent throughput for arbitrarily-large pattern sets, state activation rates, and pattern match rates remains a challenge. In this article, we present NAPOLY (Non-Deterministic Automata Processor OverLaY), an FPGA overlay and associated compiler. A common limitation of prior efforts is a limit on NFA size for achieving the advertised throughput. NAPOLY is optimized for fast re-programming to permit practical time-division multiplexing of the hardware and permit high asymptotic throughput for NFAs of unlimited size, unlimited state activation rate, and high pattern reporting rate. NAPOLY also allows for offline generation of configurations having tradeoffs between state capacity and transition capacity. In this article, we (1) evaluate NAPOLY using benchmarks packaged in the ANMLZoo benchmark suite, (2) evaluate the use of an SAT solver for allocating physical resources, and (3) compare NAPOLY’s performance against existing solutions. NAPOLY performs most favorably on larger benchmarks, benchmarks with higher state activation frequency, and benchmarks with higher reporting frequency. NAPOLY outperforms the fastest of the CPU and GPU implementations in 10 out of 12 benchmarks.

The spectral correlation density (SCD) is an important tool in cyclostationary signal detection and classification. Even using efficient techniques based on the fast Fourier transform (FFT), real-time implementations are challenging because of the high computational complexity. A key dimension for computational optimization lies in minimizing the wordlength employed. In this article, we analyze the relationship between wordlength and signal-to-quantization noise in fixed-point implementations of the SCD function. A canonical SCD estimation algorithm, the FFT accumulation method (FAM) using fixed-point arithmetic, is studied. We derive closed-form expressions for SQNR and compare them at wordlengths ranging from 14 to 26 bits. The differences between the calculated SQNR and bit-exact simulations are less than 1 dB. Furthermore, an HLS-based FPGA design is implemented on a Xilinx Zynq UltraScale+ XCZU28DR-2FFVG1517E RFSoC. Using less than 25% of the logic fabric on the device, it consumes 7.7 W total on-chip power and has a power efficiency of 12.4 GOPS/W, which is an order of magnitude improvement over an Nvidia Tesla K40 graphics processing unit (GPU) implementation. In terms of throughput, it achieves 50 MS/sec, which is a speedup of 1.6 over a recent optimized FPGA implementation.