Beethoven vs Alternatives

Beethoven sits at the intersection of hardware design and software integration. Understanding how it compares to alternatives helps you choose the right tool for your project.

Quick Comparison

Approach	Hardware Control	SW Integration	Multi-Platform	Learning Curve
Beethoven	Full (Chisel/Verilog)	Automatic C++	AWS F2, Kria, Sim	Medium
HLS (Vitis/Intel)	Limited (C++)	Manual	Platform-specific	Low-Medium
OpenCL/SYCL	Very Limited	Standardized	Multi-vendor	Medium
Chisel (Manual)	Full	Manual	Any	Medium-High
Pure Verilog	Full	Manual	Any	High

Beethoven vs High-Level Synthesis (HLS)

HLS (Xilinx Vitis, Intel HLS Compiler)

What HLS Does Well:

Low barrier to entry (C/C++ to hardware)
Automatic pipelining and optimization
Integrated with vendor tools (Vivado, Quartus)
Good for algorithm exploration

Where Beethoven Excels:

Hardware Control: Full access to Chisel primitives, custom protocols, and timing
Multi-Platform: Single design targets AWS F2, Kria, simulation without vendor lock-in
Software Integration: Automatic C++ bindings match your hardware interface exactly
Composability: Modular hardware with type-safe protocol negotiation (Diplomacy)
Customization: Define custom memory interfaces, not limited to vendor templates

When to Use HLS:

Rapid prototyping from existing C++ algorithms
Team lacks hardware design experience
Vendor-specific optimizations are critical

When to Use Beethoven:

Need precise control over microarchitecture
Deploying across multiple FPGA platforms
Building complex multi-core accelerators
Integrating custom protocols or interfaces

Example: Matrix Multiply

Vitis HLS Beethoven (Chisel)

Vitis HLS	Beethoven (Chisel)
`void matmul( int A[N][N], int B[N][N], int C[N][N] ) { #pragma HLS INTERFACE m_axi port=A #pragma HLS INTERFACE m_axi port=B #pragma HLS INTERFACE m_axi port=C for(int i=0; i<N; i++) for(int j=0; j<N; j++) for(int k=0; k<N; k++) C[i][j] += A[i][k]*B[k][j]; }`	`class MatMul(implicit p: Parameters) extends AcceleratorCore { val io = BeethovenIO( new AccelCommand("matmul") { val a_addr = Address() val b_addr = Address() val c_addr = Address() val n = UInt(32.W) }, EmptyAccelResponse() ) val a_reader = getReaderModule("a") val b_reader = getReaderModule("b") val c_writer = getWriterModule("c") // Full control over datapath // Custom memory access patterns // Explicit parallelism }`

void matmul(
  int A[N][N],
  int B[N][N],
  int C[N][N]
) {
#pragma HLS INTERFACE m_axi port=A
#pragma HLS INTERFACE m_axi port=B
#pragma HLS INTERFACE m_axi port=C
  for(int i=0; i<N; i++)
    for(int j=0; j<N; j++)
      for(int k=0; k<N; k++)
        C[i][j] += A[i][k]*B[k][j];
}

class MatMul(implicit p: Parameters)
  extends AcceleratorCore {
  val io = BeethovenIO(
    new AccelCommand("matmul") {
      val a_addr = Address()
      val b_addr = Address()
      val c_addr = Address()
      val n = UInt(32.W)
    },
    EmptyAccelResponse()
  )

  val a_reader = getReaderModule("a")
  val b_reader = getReaderModule("b")
  val c_writer = getWriterModule("c")

  // Full control over datapath
  // Custom memory access patterns
  // Explicit parallelism
}

Trade-offs:

HLS: Faster to write, less control, vendor-specific
Beethoven: More verbose, full control, portable

Beethoven vs OpenCL/SYCL

OpenCL for FPGAs (Intel FPGA SDK, Xilinx Vitis)

What OpenCL Does Well:

Standardized API across CPU/GPU/FPGA
Abstract hardware details from application developer
Good for heterogeneous computing workflows

Where Beethoven Excels:

No Runtime Overhead: Direct hardware calls, no OpenCL runtime
Custom Interfaces: Not limited to OpenCL memory model
Explicit Control: Specify memory channels, scratchpads, communication topology
Simulation: Fast co-simulation without vendor tools
Lower Latency: ~10µs command latency vs OpenCL's millisecond overhead

When to Use OpenCL:

Portability across CPU/GPU/FPGA is critical
Team already uses OpenCL for GPU acceleration
Need mature ecosystem (libraries, debuggers)

When to Use Beethoven:

Latency-sensitive applications (microsecond response times)
Custom hardware architectures (scratchpads, custom interconnect)
Tight integration with host application
Don't need GPU/CPU portability

Beethoven vs Raw Chisel

Chisel with Manual Integration

What Raw Chisel Gives You:

Full hardware expressiveness
Same Chisel benefits (type safety, generators)
Freedom to structure integration

What Beethoven Adds:

Automatic C++ Bindings: No manual command encoding
Memory Abstractions: Readers, Writers, Scratchpads with automatic protocol generation
Multi-Platform Support: Platform abstraction (AWS F2, Kria, Simulation)
Co-Simulation Infrastructure: Verilator/VCS/Icarus integration out-of-the-box
Floorplanning: Automatic SLR partitioning for multi-die FPGAs

When to Use Raw Chisel:

Building non-accelerator hardware (processors, peripherals, ASICs)
Full control over build flow and integration
Custom simulator or FPGA platform not supported by Beethoven

When to Use Beethoven:

Building host-accelerator systems
Need fast iteration (automatic software generation)
Targeting supported platforms (AWS F2, Kria)
Want turnkey simulation environment

Beethoven vs Pure Verilog/SystemVerilog

Manual Verilog Development

What Verilog Gives You:

Maximum hardware control
Industry-standard language
Universal tool support

What Beethoven Adds:

Generator Power: Parameterized modules with Scala metaprogramming
Type Safety: Compile-time checking of connections and protocols
Automatic C++ Bindings: No manual register map or command encoding
Platform Abstraction: Same design runs on AWS F2, Kria, simulation
Faster Development: Less boilerplate, automatic interconnect generation

When to Use Verilog:

Team lacks Scala/Chisel experience and unwilling to learn
Need industry-standard language for IP delivery
Working with legacy Verilog codebase

When to Use Beethoven:

Building new accelerators from scratch
Want hardware generators (parameterized designs)
Need fast software integration
Targeting multiple platforms

Verilog Integration

Beethoven supports Verilog via Chisel Blackboxes. You can integrate existing Verilog IP into Beethoven accelerators.

Comparison Matrix

Design Productivity

Feature	Beethoven	HLS	OpenCL	Raw Chisel	Verilog
Time to First Working Design	⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐	⭐⭐	⭐
Hardware Expressiveness	⭐⭐⭐⭐⭐	⭐⭐	⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐
Software Integration Effort	⭐⭐⭐⭐⭐	⭐⭐	⭐⭐⭐⭐	⭐	⭐
Multi-Platform Support	⭐⭐⭐⭐	⭐⭐	⭐⭐⭐⭐	⭐⭐	⭐⭐
Simulation Speed	⭐⭐⭐⭐	⭐⭐⭐	⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐

Performance Characteristics

Metric	Beethoven	HLS	OpenCL	Notes
Command Latency	~10µs	~10µs	~1ms	OpenCL has runtime overhead
Peak Throughput	Hardware-limited	Hardware-limited	Hardware-limited	All reach same peak if tuned
Resource Utilization	Excellent (manual control)	Good (auto-optimized)	Good	HLS may over-provision
Power Efficiency	Excellent	Good	Good	Manual designs typically more efficient

Decision Guide

Choose Beethoven if you:

✅ Need precise control over hardware microarchitecture
✅ Are building custom accelerators for compute-intensive tasks
✅ Want automatic C++ integration without manual register maps
✅ Need to support multiple FPGA platforms (AWS F2, Kria)
✅ Have (or want to learn) Chisel/Scala experience
✅ Value fast co-simulation for debugging
✅ Are comfortable with hardware-software co-design

Choose HLS if you:

✅ Have existing C++ algorithms to accelerate
✅ Team has limited hardware design experience
✅ Need rapid prototyping and iteration
✅ Vendor-specific optimizations are acceptable
✅ Don't need multi-platform portability

Choose OpenCL if you:

✅ Need portability across CPU/GPU/FPGA
✅ Already use OpenCL for GPU acceleration
✅ Can tolerate millisecond command latencies
✅ Prefer standardized APIs over custom integration

Choose Raw Chisel if you:

✅ Building non-accelerator hardware (ASICs, processors)
✅ Need custom build flows or unsupported platforms
✅ Don't need automatic software generation
✅ Want maximum flexibility in integration

Choose Verilog if you:

✅ Team is Verilog-only and unwilling to adopt new tools
✅ Need industry-standard IP delivery format
✅ Working with large legacy Verilog codebase

Hybrid Approaches

Beethoven is not mutually exclusive with other approaches:

Beethoven + Verilog:

Use Chisel Blackboxes to integrate Verilog IP
Best of both worlds: generator power + existing IP

Beethoven + HLS:

Use HLS to generate compute kernels (Verilog output)
Wrap HLS kernels in Beethoven infrastructure for multi-platform support

Beethoven + OpenCL:

OpenCL for GPU/CPU path, Beethoven for FPGA path
Shared high-level application logic

Getting Started

If Beethoven sounds like the right fit, start here:

Getting Started Guide - Setup and first accelerator
Vector Addition Example - Complete walkthrough
Hardware Overview - Architecture and design patterns

Quick Comparison​

Beethoven vs High-Level Synthesis (HLS)​

HLS (Xilinx Vitis, Intel HLS Compiler)​

Example: Matrix Multiply​

Beethoven vs OpenCL/SYCL​

OpenCL for FPGAs (Intel FPGA SDK, Xilinx Vitis)​

Beethoven vs Raw Chisel​

Chisel with Manual Integration​

Beethoven vs Pure Verilog/SystemVerilog​

Manual Verilog Development​

Comparison Matrix​

Design Productivity​

Performance Characteristics​

Decision Guide​

Choose Beethoven if you:​

Choose HLS if you:​

Choose OpenCL if you:​

Choose Raw Chisel if you:​

Choose Verilog if you:​

Hybrid Approaches​

Getting Started​

Quick Comparison

Beethoven vs High-Level Synthesis (HLS)

HLS (Xilinx Vitis, Intel HLS Compiler)

Example: Matrix Multiply

Beethoven vs OpenCL/SYCL

OpenCL for FPGAs (Intel FPGA SDK, Xilinx Vitis)

Beethoven vs Raw Chisel

Chisel with Manual Integration

Beethoven vs Pure Verilog/SystemVerilog

Manual Verilog Development

Comparison Matrix

Design Productivity

Performance Characteristics

Decision Guide

Choose Beethoven if you:

Choose HLS if you:

Choose OpenCL if you:

Choose Raw Chisel if you:

Choose Verilog if you:

Hybrid Approaches

Getting Started