FPGA Accelerator for 3DES Algorithm Based on OpenCL

Table of Contents

1. Introduction

In the fields of digital currency, blockchain, and cloud data encryption, traditional software-based encryption and decryption methods face significant challenges including slow computation speeds, high host resource consumption, and excessive power usage. While FPGA-based implementations using Verilog/VHDL offer hardware acceleration, they suffer from long development cycles and difficult maintenance.

This research presents an innovative OpenCL-based FPGA accelerator design for 3DES algorithm that addresses these limitations through sophisticated optimization strategies including pipeline parallel architecture, data storage adjustment, bit-width improvement, instruction stream optimization, kernel vectorization, and compute unit replication.

2. 3DES Algorithm Principles

2.1 DES Algorithm

The DES (Data Encryption Standard) algorithm operates on 64-bit blocks using a 56-bit key through 16 rounds of Feistel network operations. The core mathematical operation can be represented as:

$L_i = R_{i-1}$

$R_i = L_{i-1} \oplus f(R_{i-1}, K_i)$

Where $L_i$ and $R_i$ represent the left and right halves of the data block, $K_i$ is the round key, and $f$ is the Feistel function involving expansion, key mixing, substitution, and permutation operations.

2.2 3DES Algorithm Structure

3DES enhances security by applying DES three times with either two or three different keys. The encryption process follows:

$C = E_{K3}(D_{K2}(E_{K1}(P)))$

Where $E$ represents encryption, $D$ represents decryption, $P$ is plaintext, $C$ is ciphertext, and $K1$, $K2$, $K3$ are the three 56-bit keys. This structure provides 48 rounds of encryption with 168-bit effective key length.

3. OpenCL-based FPGA Accelerator Design

3.1 Architecture Overview

The proposed accelerator employs a comprehensive pipeline parallel structure with 48 iterations specifically designed for 3DES algorithm. The architecture consists of two main modules: data transmission module and algorithm encryption module, optimized for maximum throughput on Intel Stratix 10 GX2800 FPGA.

3.2 Data Transmission Optimization

The data transmission module implements two key strategies:

• Data Storage Adjustment: Optimizes memory access patterns to reduce latency
• Data Bit-width Improvement: Increases data path width to maximize bandwidth utilization

These optimizations achieve over 85% actual kernel bandwidth utilization, significantly higher than conventional implementations.

3.3 Algorithm Encryption Module

The encryption module employs instruction stream optimization to create a deeply pipelined parallel architecture. Key features include:

• 48-stage pipeline for 3DES rounds
• Parallel key scheduling
• Optimized S-box implementations
• Minimized data dependencies between rounds

3.4 Performance Enhancement Strategies

Additional performance improvements are achieved through:

• Kernel Vectorization: Utilizing SIMD operations for parallel data processing
• Compute Unit Replication: Multiple parallel compute units for increased throughput
• Memory Access Optimization: Coalesced memory accesses and local memory utilization

4. Experimental Results

The experimental evaluation demonstrates remarkable performance achievements:

The FPGA implementation achieves 111.801 Gb/s throughput while consuming significantly less power than both CPU and GPU implementations, demonstrating superior energy efficiency for cryptographic applications.

5. Technical Analysis

Expert Analysis: Four-Step Critical Assessment

一针见血 (Cutting to the Chase)

This research delivers a brutal reality check to traditional cryptographic implementations. The 372x performance improvement over modern CPUs isn't just incremental—it's architectural disruption. The authors have essentially demonstrated that for 3DES workloads, general-purpose processors are fundamentally inefficient, and even GPUs can't match FPGA's energy efficiency for this specific task.

逻辑链条 (Logical Chain)

The performance breakthrough follows a clear optimization hierarchy: First, they attacked memory bandwidth utilization through data storage adjustments (addressing the memory wall problem). Second, they implemented deep pipelining to exploit the 48-round 3DES structure. Third, they applied vectorization and compute unit replication to maximize parallel processing. This systematic approach mirrors optimization strategies seen in high-performance computing literature, particularly the Roofline Model analysis used in Berkeley's ASPIRE project.

亮点与槽点 (Highlights and Limitations)

Highlights: The 644x energy efficiency improvement is staggering and has real implications for data center operations. The use of OpenCL rather than traditional HDL makes this approach accessible to software engineers. The comparison against both CPU and GPU provides comprehensive benchmarking.

Limitations: The paper focuses exclusively on 3DES, which is being phased out in favor of AES in many applications. There's limited discussion about scalability to other algorithms. The Intel Stratix 10 GX2800 is a high-end FPGA, making cost-effectiveness for smaller deployments questionable.

行动启示 (Actionable Insights)

For cloud providers and financial institutions still using 3DES, this research provides a clear migration path to FPGA acceleration. The OpenCL approach significantly lowers the barrier to entry compared to traditional FPGA development. Organizations should consider FPGA-based cryptographic acceleration for high-volume transaction processing and consider this architecture as a template for accelerating other symmetric encryption algorithms.

Original Analysis (400 words)

This research represents a significant advancement in cryptographic acceleration that bridges the gap between software accessibility and hardware performance. The authors' approach of using OpenCL for FPGA development addresses a critical pain point in high-performance computing: the expertise barrier for hardware acceleration. As noted in the Khronos Group's OpenCL specification, this framework enables "parallel programming of heterogeneous systems using a portable, open standard," making accelerated computing accessible to mainstream developers.

The 111.8 Gb/s throughput achieved demonstrates the effectiveness of the pipeline parallel architecture for cryptographic workloads. This performance aligns with trends observed in other domain-specific architectures, such as Google's TPU for neural networks or Intel's Habana Labs AI processors. The key insight here is that cryptographic algorithms, with their regular structure and deterministic execution patterns, are particularly well-suited to FPGA acceleration.

Compared to traditional HDL-based approaches documented in IEEE Transactions on VLSI Systems, the OpenCL implementation offers significant development efficiency advantages. However, as research from the University of Toronto's FPGA group has shown, there's typically a performance penalty when using high-level synthesis compared to hand-optimized RTL. The fact that this implementation still achieves superior performance to both CPU and GPU suggests exceptionally effective optimization strategies.

The energy efficiency results (644x improvement over CPU) are particularly compelling given the growing importance of computational sustainability. As data centers increasingly face power constraints, approaches that deliver massive performance per watt improvements will become essential. This research demonstrates that for specific computational patterns like cryptographic algorithms, FPGAs can provide order-of-magnitude advantages over general-purpose architectures.

However, the focus on 3DES raises questions about long-term relevance. With NIST deprecating 3DES for many applications and transitioning to AES, the applicability of these specific optimizations to modern cryptographic standards deserves further investigation. The architectural patterns and optimization strategies, however, are likely transferable to AES and other symmetric encryption algorithms.

6. Code Implementation

OpenCL Kernel Example

__kernel void triple_des_encrypt(
    __global const uint8_t *input,
    __global uint8_t *output,
    __constant uint32_t *key_schedule,
    const uint num_blocks)
{
    int gid = get_global_id(0);
    if (gid >= num_blocks) return;
    
    // Load 64-bit block
    uint64_t block = *((__global uint64_t*)(input + gid * 8));
    
    // 3DES Encryption: E_K3(D_K2(E_K1(P)))
    block = des_encrypt(block, key_schedule, 0);      // First DES with K1
    block = des_decrypt(block, key_schedule, 16);     // Second DES with K2  
    block = des_encrypt(block, key_schedule, 32);     // Third DES with K3
    
    // Store result
    *((__global uint64_t*)(output + gid * 8)) = block;
}

uint64_t des_encrypt(uint64_t block, __constant uint32_t *keys, int key_offset)
{
    // Initial permutation
    block = initial_permutation(block);
    
    uint32_t left = (uint32_t)(block >> 32);
    uint32_t right = (uint32_t)block;
    
    // 16 Feistel rounds
    #pragma unroll
    for (int i = 0; i < 16; i++) {
        uint32_t temp = right;
        right = left ^ feistel_function(right, keys[key_offset + i]);
        left = temp;
    }
    
    // Final permutation
    return final_permutation(((uint64_t)right << 32) | left);
}

7. Future Applications

The architectural approach demonstrated in this research has broad applicability beyond 3DES encryption:

• Blockchain and Cryptocurrency: High-frequency trading platforms and mining operations could leverage similar FPGA acceleration for cryptographic operations.
• 5G Security: The pipeline architecture could be adapted for 5G encryption standards in base station processing.
• Edge Computing: Lower-power FPGA implementations could provide cryptographic acceleration for IoT devices and edge servers.
• Post-Quantum Cryptography: The optimization strategies could be applied to emerging post-quantum cryptographic algorithms.
• Multi-Algorithm Accelerators: Future work could explore dynamically reconfigurable FPGA designs that support multiple encryption algorithms.

Research directions include exploring the application of these optimization techniques to AES-GCM, ChaCha20-Poly1305, and other modern encryption standards, as well as investigating automated optimization tools that can apply similar transformations to arbitrary cryptographic algorithms.

8. References

1. K. Group, "The OpenCL Specification," Khronos Group, 2020.
2. National Institute of Standards and Technology, "Recommendation for Triple Data Encryption Algorithm (TDEA) Block Cipher," NIST SP 800-67Rev2, 2017.
3. J. Cong et al., "High-Level Synthesis for FPGAs: From Prototyping to Deployment," IEEE Transactions on CAD, 2011.
4. M. Papadonikolakis et al., "Performance Comparison of GPU and FPGA Architectures for Cryptography," SAMOS, 2010.
5. A. M. et al., "FPGA-based Accelerators of Cryptographic Algorithms," IEEE Transactions on Computers, 2013.
6. Intel Corporation, "Intel FPGA SDK for OpenCL Programming Guide," 2020.
7. Xilinx, "SDAccel Development Environment User Guide," 2019.
8. W. Jiang et al., "A Survey of FPGA-Based Cryptographic Computing," ACM Computing Surveys, 2021.