Overview

The Abstraction Gap and Model Fragmentation

The abstraction gap and model fragmentation are two key drivers of hardware design-flow complexity. The abstraction difference between a natural-language design specification and the register-transfer-level (RTL) model that implements it is enormous, and only increasing as designs become larger and more complex. It’s natural to prioritize the RTL model, since that is required to get to implementation. However, because RTL models are detailed and execute slowly, they don’t do a good job of meeting the needs of adjacent disciplines, such as firmware. This delays how early software teams obtain access to a representation of the design, and limits the platforms on which they can work to fast hardware emulation or prototyping environments.

Over time, new requirements have resulted in the development of new domain-specific languages and language-like class libraries. SystemVerilog, PSS, UPF, IP-XACT, SystemC, and others all provide features that address pain points of hardware development. Unfortunately, this has also led to a very fragmented ecosystem with loosely-integrated languages and methodologies and complex build flows.

Considering the Ecosystem

All of these innovations have primarily come from a hardware-design perspective, and focus on enabling hardware-design flows. That, in itself, is a challenge given the relative sizes of the hardware- and software-engineering ecosystems. While it’s difficult to find accurate detailed data, US Bureau of Labor Statistics reports a labor-market size of 76,800 for hardware engineers vs a labor-market size of 1,534,790 for software engineers (inclusive of all sub-disciplines in both cases).

Given that the collective challenge is a combined hardware and software system, getting buy-in from both disciplines is critical. It seems quite unlikely that a language created to serve the unique requirements of the ecosystem’s minority can serve the combined needs and gain the acceptance of the majority.

Why Zuspec?

Zuspec adopts Python as its starting point, and embeds hardware semantics into that ecosystem. The result is a platform that is familiar to software engineers, offers high productivity for hardware engineering, and has the goal of increasing the ability to share artifacts across the disciplines.

Why Python?

Many factors are involved in selecting a language for any purpose: key language features, tool ecosystem, relevant libraries, as well as the community around the language. Applying an existing language to the semantics of another domains raises another factor to consider: flexibility of the language.

Python is a popular language overall, holding the top spot in many rankings for several consecutive years, and being ranked highly for many years before that. Language popularity has a direct bearing on language technical features. Larger communities produce more ideas for using a language and, thus, a larger library ecosystem. Larger communities more-rapidly produce and refine adjacent technologies, such as code development and package management tools, and new language features.

Popularity often builds upon itself, and there is evidence that AI is acting as a driver of Python’s popularity. Specifically, AI assistants are often reported to be more effective with Python than with other languages. This has the effect of drawing more developers to Python, which increases the available code in Python, which more-rapidly increases the quality of results with Python.

Multi-Abstraction Modeling

Zuspec provides a unified framework for modeling hardware at multiple levels of abstraction:

  • Transfer Function Level: High-level behavioral models for early software development and architectural exploration

  • Transaction Level: Cycle-approximate models for performance analysis

  • Register Transfer Level: Synthesizable hardware descriptions

This multi-abstraction approach allows teams to:

  • Start software development earlier with fast executable models

  • Refine designs incrementally from high-level to detailed implementations

  • Maintain consistency across abstraction levels

  • Reuse test content across different model abstractions

Key Features

  • Python-Native: Leverage the entire Python ecosystem including libraries, tools, and IDE support

  • Multiple Backends: Generate SystemVerilog RTL, C/C++ software models, and verification frameworks

  • Dataclass-Based: Use familiar Python dataclass syntax for hardware modeling

  • Type-Safe: Static type checking for hardware constructs

  • Extensible: Plugin architecture for custom backends and transformations

Getting Started

To get started with Zuspec, see the Installation guide and explore the Zuspec Dataclasses documentation for the core language.

For a comprehensive overview, see the Papers & Publications section which includes the full academic paper on Zuspec.