Introduction

This Section will Introduce you the Ara Vector Processor.

Introduction to Ara

Ara is a modular, open-source vector processing unit designed to augment RISC-V processors with high-performance, energy-efficient vector computation. It is developed by the PULP platform at ETH Zurich and the University of Bologna.

At its core, Ara is a scalable vector accelerator compliant with the RISC-V Vector Extension 1.0 (RVV). It is specifically designed to target data-parallel workloads in embedded and high-performance computing environments. Ara is built to plug into the CVA6 RISC-V core, and is suitable for system-on-chip (SoC) designs that require vector processing capabilities for applications such as:

  • Signal processing

  • Linear algebra

  • Machine learning kernels

  • Computer vision

Ara can be configured with a power-of-2 number of parallel vector lanes ranging from 2 to 16. Each lane contains a slice of the Vector Register File (VRF), a SIMD FPU, and a SIMD ALU/Multiplier.

Since Multiply-and-Accumulate instructions can be executed in one cycle by each FPU, the maximum performance of Ara is 2 * #Lanes 64-bit packets/cycle, where each 64-bit packet can contain from one 64-bit element to eight 8-bit elements, processed in SIMD fashion.

Architectural Overview

Ara Top level Image

Ara’s top level diagram

Ara is composed of the following major components:

  • Vector Functional Units (VFUs): Includes ALUs, FPUs, multipliers, mask units, and slide units, each operating across multiple lanes in parallel.

  • Vector Lanes: The architecture is scalable to multiple lanes. Each lane processes one slice of the data, providing parallelism at the element level.

  • Dispatcher & Sequencer: These components decode and schedule vector instructions, ensuring synchronization and legal execution patterns.

  • Register File (VRF): Ara contains a large vector register file, partitioned across lanes for bandwidth scalability.

  • Memory Interface: Vector loads and stores are issued via AXI-compatible memory accesses, either from DRAM or TCDM (depending on system integration).

Ara is modular by construction: users can configure the number of lanes, supported datatypes (int/fixed/fp), and optional features like floating-point extensions or reductions.

Target Use Cases

Ara is ideal for:

  • Academic research into vector architectures and compilers

  • Hardware/software co-design

  • Custom accelerators in SoC designs

  • FPGA-based prototyping of RVV software

Ara for Non-Experts

What Is Ara?

Think of Ara as a muscle boost for a processor, specialized in handling repetitive data-heavy tasks—like multiplying a thousand numbers in parallel or transforming images efficiently.

Instead of performing one operation at a time, Ara handles many operations at once using vectors (think: long lists of numbers). This is similar to how modern GPUs work for games and machine learning—but in a way that is more tailored to embedded systems and open-source hardware.

Why Is Ara Useful?

Most general-purpose CPUs process one or two pieces of data per cycle. Ara can process dozens or hundreds of values in parallel. This makes it much faster and more energy-efficient when dealing with structured, repetitive workloads like:

  • Filtering an audio signal

  • Applying a blur effect on an image

  • Computing dot products in a neural network layer

Ara plugs into a RISC-V core like an add-on, offloading these “bulk” computations.