da4ml 0.5.0b0

Distributed Arithmetic for Machine Learning

da4ml: Distributed Arithmetic for Machine Learning

da4ml is a library for implementing distributed arithmetic (DA) based algorithms for ultra-low latency machine learning (ML) applications on FPGAs. It as two major components:

• A fast and performant constant-matrix-vector multiplications (CMVM) optimizer to implement them as efficient adder trees. Common sub-expressions elimination (CSE) with graph-based pre-optimization are performed to reduce the firmware footprint and improve the performance.
• Low-level symbolic tracing frameworks for generating combinational/fully pipelined logics in HDL or HLS code. For fully pipelined networks, da4ml can generate the firmware for the whole network standalone. Alternatively, da4ml be used as a plugin in hls4ml to optimize the CMVM operations in the network.

Key Features

• Optimized Algorithms: Comparing to hls4ml's latency strategy, da4ml's CMVM implementation uses no DSO and consumes up to 50% less LUT usage.
• Fast code generation: da4ml can generate HDL for a fully pipelined network in seconds. For the same models, high-level synthesis tools like Vivado/Vitis HLS can take up to days to generate the HDL code.
• Low-level symbolic tracing: As long as the operation can be expressed by a combination of the low-level operations supported, adding new operations is straightforward by "replaying" the operation on the symbolic tensor provided. In most cases, adding support for a new operation/layer takes just a few lines of code in numpy flavor.
• Automatic model conversion: da4ml can automatically convert models trained in HGQ2 <https://github.com/calad0i/hgq2>_.
• Bit-accurate Simulation: All operation in da4ml is bit-accurate, meaning the generated HDL code will produce the same output as the original model. da4ml's computation is converted to a RISC-like, instruction set level intermediate representation, distributed arithmetic instruction set (DAIS), which can be easily simulated in multiple ways.
• hls4ml integration: da4ml can be used as a plugin in hls4ml to optimize the CMVM operations in the network by setting strategy='distributed_arithmetic' for the strategy of the Dense, EinsumDense, or Conv1/2D layers.

Installation

pip install da4ml

Getting Started

See the Getting Started guide for a quick introduction to using da4ml.