Understanding Hardware Acceleration Development: HLS vs. Beethoven vs. Other Approaches

Hardware acceleration has become increasingly important in the post-Moore’s law era, with various tools and frameworks available for development. Let’s explore the main approaches and understand where each fits best in the acceleration ecosystem.

High-Level Synthesis (HLS)

High-Level Synthesis allows developers to write hardware descriptions using high-level languages like C/C++. While this approach has gained popularity, it comes with both benefits and limitations.

Benefits of HLS

• Write hardware functionality in familiar C/C++
• Rapid prototyping capabilities
• Lower barrier to entry for software developers
• Good for simple accelerator designs

Limitations

Looking at a memory copy benchmark, HLS achieved 2.82 GB/s throughput, while Beethoven’s implementation reached 6.69 GB/s. This performance difference stems from how HLS handles memory operations:

• Uses a single AXI ID for all memory transactions
• Forces in-order completion of memory operations
• Has limited memory transaction parallelism
• Requires deep understanding of pragmas for optimization

Our experiments showed HLS:

• Performs 4KB memcpy using 4 requests at 16 beats per request
• Uses a single Transaction ID that gets reused over time
• Completes requests strictly in-order

Domain-Specific Languages (DSLs)

Domain-Specific Languages take a different approach by focusing on specific application domains.

Popular DSLs

• Halide: Optimized for image processing pipelines
• Spatial: Provides hardware-like constructs in a high-level language
• CFU Playground: Focused on TinyML acceleration

While DSLs excel in their specific domains, they’re limited by their specialization. They work best when your application fits perfectly within their target domain.

Beethoven’s Novel Approach

Beethoven takes a fundamentally different approach by focusing on scalable, multi-core accelerator design while providing high-level abstractions.

Key Innovations

1. Clean Memory Abstractions

cppCopy// Beethoven's memory primitives
class Core {
    Reader input_stream;  // Streaming reads with prefetch
    Writer output_stream; // Streaming writes
    Scratchpad local_mem; // Local storage
    
    void process() {
        // High-level memory access with optimal performance
    }
};

2. Multi-Core First Design

Beethoven organizes accelerators hierarchically:

• Core: Basic processing unit
• System: Group of identical cores
• Accelerator: Collection of systems

3. Smart Resource Management

• Automatically manages FPGA resources
• Switches between BRAM/URAM based on utilization
• Handles multi-die FPGA complexities
• Generates optimal placement constraints

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The hardware acceleration landscape offers different tools for different needs. While HLS provides accessibility and DSLs offer domain-specific optimization, Beethoven fills a crucial gap by providing a framework for building scalable, multi-core accelerators with sophisticated memory handling and platform independence.

Beethoven’s approach shows that by providing the right abstractions, we can achieve both ease of development and high performance. Its success in real-world applications demonstrates that we don’t need to sacrifice performance for productivity in hardware acceleration development.