Hardware Accelerator

What Is a Hardware Accelerator?

Hardware accelerators are purpose-built designs that accompany a processor for accelerating a specific function or workload (also sometimes called “co-processors”). Since processors are designed to handle a wide range of workloads, processor architectures are rarely the most optimal for specific functions or workloads.

Systems composed of processors and hardware accelerators bring the benefits of software programmability for much of the software stack run on the processor, while delivering the superior power and performance for functions run on a purpose-built hardware accelerator.

Designers are increasingly turning to hardware accelerators as a means to improve the power, performance, and area (PPA) of processor-based systems independent of semiconductor process scaling.

Digital signal processing (DSP) functions like video codecs, communications error corrections and filtering, and artificial intelligence (AI) learning and inferencing algorithms are examples of functions that benefit significantly, in terms of PPA, when implemented as hardware accelerators.

 

Benefits of Hardware Accelerators

Hardware accelerators are employed to deliver higher performance and lower power than equivalent functions running on a general-purpose processor. They offer the following benefits:

Performance Improvement

Because hardware accelerators are purpose-built to handle a specific function, their resources can be right-sized with data precision that is designed to exactly match the needs of the algorithm rather than processors that offer resources in 8, 16, 32, or 64-bits. For example, an algorithm requiring only 10-bit integer precision can utilize faster right-sized arithmetic resources versus a processor that may only have 32-bit resources. Since many DSP algorithms have repetitive multiply-accumulate functions, this performance difference can be dramatic. Hardware accelerators also benefit from shedding the complexity and execution time required for instruction fetch, decode, branch prediction, data caching, and decaching operations that are core functions in modern processor architectures.

Lower Power

Similar to the previous discussion on performance improvement, hardware accelerators deliver dramatic reductions in power by incorporating right-sized arithmetic resources and shedding hardware involved in managing a stream of instructions and associated data. Hardware accelerators can better take advantage of power optimizations to reduce power as a dedicated function hardware accelerator can be clock- or power-gated to reduce power without affecting other functions running on the processor.

How Do Hardware Accelerators Work?

Hardware accelerators are custom designed to perform a single task versus processors that must handle any task defined by a software program. As such, their operation (how they work) will vary depending on what function is captured.

Since hardware accelerators are companions to processors, they generally require communication with a processor to map a processor instruction or function to the hardware accelerator to initiate processing. Hardware accelerator data access also varies. Some accelerators have direct memory (data) access, and some rely on the processor to write/read data from/to the accelerator. Since data access can be a performance bottleneck, careful attention must be given to the design of the accelerator, processor, and memory subsystems to ensure high performance.

What Industries Can Benefit from Hardware Accelerators

Hardware accelerators are beneficial to a broad set of industries that have relied on standalone processors. Generally, processor-based designs that have performance or power targets unachievable by standalone processors may benefit from companion hardware accelerators.

Following are notable industries that benefit from hardware accelerators:

Hyperscalers

With the continued growth of image and video data stored and processed by hyperscalers, the drive to increase processing speeds and lower power consumption is constant. This dynamic fuels an increase in the design of workload-specific processing systems that rely on hardware accelerators to deliver gains in performance while lowering power consumption.

AI/ML

Artificial intelligence and machine learning (AI/ML) processing is rapidly being integrated into a wide variety of systems spanning wearable electronics that employ AI in voice recognition to advanced driver assistance systems (ADAS) that enable self-driving. In the past, graphics processors were employed to deliver performance gains over general-purpose processors. Today, in a quest for increased performance and lower power, designers increasingly turn to purpose-built hardware accelerators for systems performing AI processing.

Hardware Security and Encryption

With an IoT-based compute model, data processing occurs at the endpoints and in the data centers that power the cloud. With the constant threat of malicious agents aiming to gain access to user data, security must be designed into every component of the system—from endpoint to data center. Increasingly, designers turn to hardware accelerators to handle the encryption and decryption functions that make up the the security layers of the system.

Design Your Hardware Accelerators with Cadence

Cadence offers tools for every step in the hardware accelerator design process. From Stratus High-Level Synthesis, to Genus Logic Synthesis, Innovus Implementation System, Conformal Low Power, and Joules RTL Power Analysis, Cadence tools speed the design of hardware accelerators from C++ to GDSII.

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