What Are Coprocessors?

Coprocessors are specialized processing units designed to handle particular computational tasks more efficiently than a general-purpose central processing unit (CPU) can perform them alone. By offloading specific workloads to a dedicated circuit optimized for that class of operation, coprocessors reduce the CPU's burden, lower power consumption for the targeted task, and often achieve throughput many orders of magnitude higher than software running on the main processor. The term covers a broad spectrum of designs, from the floating-point units (FPUs) that became standard on desktop processors in the 1980s to the neural processing units (NPUs) now integrated into consumer-grade system-on-chip (SoC) devices.

The architectural concept of the coprocessor emerged from early microprocessor systems in which mathematical operations were too slow to handle in software but too infrequent to justify dedicating the entire processor to them. Intel's 8087 numeric coprocessor, introduced in 1980 to pair with the 8086 CPU, exemplified this approach by handling IEEE 754 floating-point arithmetic in hardware at speeds roughly 100 times faster than equivalent software routines.

Digital Arithmetic Acceleration

Digital arithmetic is the domain where coprocessors have historically provided the largest performance gains. Floating-point units handle single-precision and double-precision arithmetic conforming to IEEE Std 754, the floating-point standard that governs representation and rounding in virtually all modern computing systems. Dedicated FPU logic performs addition, subtraction, multiplication, division, and square root with hardware pipelines that can complete one operation per clock cycle in modern designs. Beyond floating-point, fixed-point multiply-accumulate (MAC) units underlie digital signal processing (DSP) coprocessors used in audio codecs, modems, and sensor fusion. The key design trade-off in arithmetic coprocessors is precision versus throughput: lower precision (8-bit or 16-bit integer operations) enables wider data paths and higher throughput, which is why AI workloads favor 8-bit integer or even 4-bit arithmetic rather than 64-bit double precision.

AI Accelerators

AI accelerators are a category of coprocessor optimized for the matrix multiply and convolution operations that dominate neural network inference and training. Graphics processing units (GPUs), originally designed for rasterization pipelines, were repurposed for general parallel computation around 2007 and became the dominant AI training hardware due to their thousands of arithmetic cores. Dedicated neural processing units in mobile SoCs, such as those described in IEEE research on SoC hardware accelerators, use dataflow architectures that minimize memory movement and reduce energy per inference well below what a GPU requires for the same task. Tensor processing units (TPUs), developed by Google and first described in 2017, use systolic array architectures to perform dense matrix multiplications at high efficiency, achieving multiple tera-operations per second at low power in cloud data centers.

Coprocessor Integration Models

Coprocessors are integrated with the host CPU through several coupling models. Tightly coupled coprocessors share the instruction stream and register file of the main processor, executing specialized instructions inserted into the normal program flow. Loosely coupled coprocessors communicate over a bus or interconnect and operate more independently, accepting command packets from the CPU. The EPFL hardware accelerators for arithmetic project explores alternative number representations that could reduce arithmetic hardware area while maintaining useful numerical range, an example of ongoing research into the arithmetic foundations of coprocessor design.

Applications

Coprocessors have applications in a wide range of computing and embedded systems, including:

• Machine learning inference in mobile devices, cameras, and IoT endpoints
• Graphics rendering and display processing in consumer and professional hardware
• Digital signal processing in software-defined radios and audio equipment
• Cryptographic acceleration for TLS/HTTPS in network infrastructure
• Scientific simulation requiring high-throughput floating-point computation