Introduction to Embedded FPGAs

The embedded field-programmable gate array (eFPGA) technology has undergone significant evolution in recent years. eFPGAs serve as a programmable fabric in addition to conventional fixed-function components when they are directly integrated into application-specific integrated circuits (ASICs) or system-on-chips (SoCs). There are various benefits to this evolution. First, it makes it possible to use silicon more effectively. Second, a greater degree of customization for end users is applicable by eFPGAs rather than depending exclusively on fixed SoC or ASIC capabilities.

eFPGAs are now being used in chips serving wireless infrastructure, artificial intelligence (AI), smart storage, and even cost-sensitive microcontrollers. For accelerating machine learning applications that require frequent updates, the flexibility of programmable logic without the cost of FPGAs is very tempting. The primary application of eFPGAs is product flexibility, and the need for this is increasing now. The future of eFPGAs is promising, and it is expected to become a viable option for various applications.

eFPGA Market Forecast

According to Allied Market Research, the global embedded FPGA market was valued at $73 million in 2021, and is projected to reach $310 million by 2031, growing at a CAGR of 15.6% from 2022 to 2031. Based on application, the market is segmented into data processing, consumer electronics, automotive, industrial, military & aerospace, telecom and others. The telecom segment acquired the largest global embedded FPGA market share in 2021.

Importance of eFPGAs in Modern Systems

The usage of eFPGA technology in chips servicing wireless infrastructure, AI, smart storage, and even cost-sensitive microcontrollers demonstrates its growing appeal in current systems.

Modern systems rely on eFPGAs because of their efficiency, flexibility, and adaptability. eFPGAs are becoming increasingly important in the continuously changing world of modern computing. They are critical in increasing the flexibility and adaptability of integrated circuits in a wide range of electronic systems. Unlike typical fixed-function hardware, eFPGAs have the various benefits of being reconfigurable, allowing designers to dynamically adapt the device’s functionality to match specific application requirements. This adaptability is useful in a wide range of applications, from signal processing and machine learning to telecommunications and data centers.

eFPGA technology is incredibly generic, it provides the flexibility of programmable logic at a fraction of the cost of FPGAs, making it particularly useful for speeding machine learning applications that require frequent updates. Incorporating FPGA functionality into ASIC enhances performance while lowering power consumption and cost at the system level, allowing the design to utilize a less costly FPGA depending on the application needs. For modern systems that demand flexibility, performance, and cost-effectiveness, eFPGA technology provides a potential answer.

eFPGAs enable developers to enhance performance, energy efficiency, and resource consumption by enabling on-the-fly customization, stimulating creativity, and expediting cutting-edge technology development. The ability to adapt to evolving computational demands positions eFPGAs as a key enabler in the pursuit of efficient and scalable solutions in the ever-changing landscape of modern systems. In today’s electronic world, eFPGAs are significant for the following main reasons.

Understanding eFPGA

Basics of eFPGAs

eFPGA is a semiconductor device capable of being programmed and re-programmed to perform particular functions once manufactured. In fact, they consist of a range of programmable logic blocks, also known as configurable logic blocks or CLBs, in which these blocks and interconnects can be configured to create unique digital circuits. Here are some basics of FPGAs:

Logic Blocks

An FPGA’s basic building components are called CLBs, which have flip-flops, LUTs, and other logic components that can be set up to perform particular tasks.

Programmable Interconnects

The channels that let signals go between various logic blocks are called interconnects, which offer flexibility since FPGAs enable designers to customize these interconnects to meet their unique needs.

Configuration

Hardware Description Languages (HDL) like Verilog or VHDL are used to set up FPGAs. These languages are used to describe the design, which is then synthesized into a netlist and converted into FPGA configuration data.

SRAM-based Configuration

Static Random-Access Memory (SRAM) is employed by the majority of FPGAs to store configuration data. This indicates that when the power is switched off, the setting is lost. The FPGA must have configuration data loaded into it each time it is powered up.

Applications

Applications for eFPGAs include digital signal processing, networking, telecommunications, image processing, and more. Because they are reprogrammable, they are also well-liked in development and prototyping processes.

Parallel Processing

Parallel processing workloads are a strong suit for eFPGAs. Since customizable logic operates in parallel, it may execute numerous operations at once, which makes it appropriate for applications requiring high computing throughput.

Hardware Description Languages (HDLs)

To specify the functioning of the intended digital circuit, designers utilize HDLs like Verilog or VHDL. The FPGA is subsequently loaded with a configuration bitstream created by synthesizing these descriptions.

Components of eFPGAs

Similar to conventional FPGAs, eFPGAs are specially integrated into bigger semiconductor devices like application-specific integrated circuits (ASICs) or system-on-chips (SoCs). The principal parts of eFPGAs are as follows:

• Configurable Logic Blocks (CLBs)
• Programmable Interconnects
• Configuration Memory
• I/O Blocks
• Embedded Memory
• Clock Management
• Hardened IP Blocks
• Configuration Interface
• JTAG (Joint Test Action Group) Interface
• Security Features

eFPGA Architecture and Design Considerations

Fine grain and coarse grain are two architectures of eFPGA. Fine grain architecture shows that the embedded re-configuration logic logic architectures have homogeneous components, which are characterized by their multi-function role in a SoC. In contrast, coarse grain includes hardware blocks such as DSP, memory, adder, simplifying the complex function implementations. These structures offer superior area efficiency while remaining flexible enough for the intended application domain. However, this heightened flexibility can lead to increased cost overhead. In the case of fine grain, it is easily modifiable and exhibits greater flexibility when compared to coarse-grained architectures.

eFPGA design considerations are the factors that impact the choice and integration of an eFPGA into an ASIC or SoC. eFPGAs are IP cores that provide programmable logic functionality within a fixed-function chip, enabling post-silicon customization, acceleration, and adaptation. Some of the main considerations for eFPGA design are:

Application and flexibility

The intended use case and the required level of reconfigurability determine the size, architecture, and features of the eFPGA fabric. For example, some applications may need more DSP blocks or memory than others or may benefit from power gating or partial reconfiguration options.

Performance and power

The speed and energy efficiency of the eFPGA depend on the process node, the voltage level, the interconnect structure, and the optimization of the mapped logic. eFPGAs typically have lower performance and higher power consumption than ASIC gates but higher performance and lower power consumption than standalone FPGAs.

Area and cost

The area and cost of the eFPGA are influenced by the amount and type of resources, the aspect ratio, the number of I/O ports, and the metal stack of the fabric. eFPGAs can reduce the area and cost of the chip compared to a two-chip solution (ASIC + FPGA) but increase them compared to a pure ASIC solution.

Security and reliability

The security and reliability of the eFPGA are affected by the programming interface, the encryption and authentication mechanisms, the error detection and correction schemes, and the testing and verification methods. eFPGAs can enhance the security and reliability of the chip by enabling updates and patches for changing threats and standards.

Typical Applications of eFPGA

Applications of eFPGA in Accelerating Data Processing

In many different applications, eFPGAs are a major factor in speeding up data processing, and they are accelerating workloads related to Artificial Intelligence (AI) and machine learning. Moreover, larger systems can be equipped with specially made FPGA accelerators to conduct functions like neural network inference, increasing processing efficiency and speed overall. As FPGA is used in data centers for AI accelerators, integrating an eFPGA into the primary ASIC increases performance by reducing ASIC-to-FPGA data delay and system power by removing redundant circuitry found on both the ASIC and the FPGA. In addition, applications such as image processing, audio processing, and telecommunications can all benefit from the speed at which eFPGAs can complete signal-processing tasks. Tailored hardware solutions have the potential to offload particular processing chores or dramatically accelerate complex signal processing methods, which is especially helpful for applications with low-latency data processing like financial trading. eFPGAs can also be used to speed up encryption and database/data compression algorithms. Particularly in secure communication systems, custom hardware implementations can improve the speed and effectiveness of these crucial data processing activities.

Furthermore, eFPGAs are excellent at parallel processing tasks. Using FPGA architecture’s parallelism, data processing jobs can be split up among several processing components, increasing throughput overall. Given this capability, they can be used to speed up feature extraction and data preparation operations in data-intensive applications like machine learning and data analytics, providing faster and more effective data analysis.

Application of eFPGAs in Edge Computing

In edge computing, where processing is done closer to the data source, eFPGAs are a good fit since they reduce latency and increase efficiency. AI and machine learning inference activities can be expedited at the edge via eFPGAs. Moreover, devices can process AI models locally by incorporating bespoke accelerators within the FPGA fabric, eliminating the requirement for continuous data transmission to centralized servers. At the edge, eFPGAs are employed for real-time image and video processing. Low-latency processing is crucial in applications like drones, augmented reality (AR) devices, and security cameras.

eFPGAs can be programmed to interface with and analyze data from different sensors in edge devices in a customized way. This flexibility is especially helpful for the Internet of Things (IoT) and industrial automation applications. Additionally, eFPGAs can help to improve edge security. To protect sensitive data processed locally, they can be utilized to construct unique cryptographic methods, secure key management, and authentication procedures.

It is worth noting that eFPGAs can be used to speed up edge data storage applications, which boosts the effectiveness of edge devices with low storage capacity, including data compression, encryption, and improved storage access. In addition, designers can incorporate unique processing modules onto eFPGAs that are intended for certain edge computing uses. This degree of personalization enables effective and optimal processing of edge workloads.

Applications of eFPGA in Wireless Communication Systems

eFPGAs are essential for improving wireless communication systems’ flexibility and performance. In the field of wireless communication, eFPGAs are extensively employed in the following applications:

• Baseband processing functions in wireless communication networks are performed by eFPGAs. For tasks like channel equalization, error correction coding, and modulation/demodulation, they can apply proprietary algorithms.
• eFPGAs offer a versatile framework for customizing and adapting protocols in wireless communication, which is especially useful for MIMO technology or software-defined radio (SDR) applications where the communication standard can be changed dynamically. Moreover, they perform functions like signal processing, interference reduction, and wireless network protocol adaptation in wireless infrastructure hardware like base stations and access points.
• Additionally, eFPGAs are essential for digital front-end processing, including beamforming, filtering, and digital up- and down-conversion. Several tasks are essential to maximize signal quality and extend communication range.
• eFPGAs can be configured to sense the radio frequency spectrum dynamically and adjust communication parameters in cognitive radio systems as well, making it possible to use the spectrum more effectively and cohabit and secure other wireless systems better.

Advantages and challenges of eFPGA

eFPGAs offer numerous benefits, including increased flexibility, lower costs, and faster time-to-market. This technology allows for customizability and adaptability to changing needs while also reducing overall expenses. Additionally, eFPGAs can speed up the development process and help companies bring their products to market more quickly. The advantages of eFPGAs are as follows:

Firstly, eFPGA delivers high performance and low latency by interfacing with ASIC through a parallel interface, allowing for high-speed operation with buses hundreds of bits wide. Additionally, it consumes less power by directly connecting to the SoC, eliminating the need for programmable I/O buffers and offering scalability to meet specific size requirements. This results in lower overall system cost as eFPGA does not require specialized PCBs or components like clock generators, power regulators, and level shifters, and it avoids bulky packages. Furthermore, eFPGA offers enhanced flexibility, allowing customization post-production, a capability not found in traditional FPGAs. Lastly, it contributes to high system reliability and yields, making it a robust choice in various applications.

Integrating eFPGAs into a SoC involves thoughtful consideration of both hardware and software interfaces between the FPGA and SoC. The integration process can be complicated by the need to support multiple clock domains, power domains, and voltage levels. Additionally, the performance of eFPGAs is often constrained by available resources like look-up tables (LUTs), registers, and embedded memories, requiring designers to strike a balance for desired performance while minimizing power consumption. The number of LUTs, registers, embedded memories, and the number of I/O ports can all be controlled when designing an eFPGA to make trade-offs between power and performance. Although soft-core eFPGA solutions have been implemented in a range of works considering the strong advantages of integration, flexibility, and portability through technology nodes, the implementation of eFPGAs can be challenging due to the complexity of the integration process and the need to balance performance and power consumption.

eFPGA Vendors

1. Achronix Semiconductor: Achronix is known for providing high-performance FPGA solutions, and they also offer embedded FPGA IP that can be integrated into ASICs or SoCs.
2. Flex Logix Technologies: Flex Logix specializes in embedded FPGA technology and provides eFPGA IP cores for integration into semiconductor devices.
3. Menta: Menta focuses on programmable logic IP, including eFPGA IP for ASIC and SoC designs.
4. QuickLogic Corporation: QuickLogic offers eFPGA solutions that can be integrated into microcontrollers and other semiconductor devices.
5. Lattice Semiconductor: Lattice is a well-known FPGA manufacturer, and while they are more recognized for their standalone FPGAs, they may offer solutions that can be embedded in certain applications.

Design and Development Tools

Programming eFPGAs

The design and development of eFPGAs require specialized tools that enable the creation of custom logic circuits. The programming of eFPGAs involves using hardware description languages (HDLs) such as VHDL and Verilog. These languages allow the designer to describe the behavior of the circuit in terms of logic gates, flip-flops, and other digital components. The HDL code is then synthesized into a netlist, a list of interconnected gates and flip-flops that implement the desired functionality. The netlist is then mapped onto the eFPGA fabric using place-and-route tools that optimize the performance, power, and area of design. Some popular open-source eFPGA design and verification tools include Yosys, VPR, Arachne, Nextpnr, and Bit Stream Generation. These tools provide a unified software platform that plugs into common development tools, making the process of programming eFPGAs more accessible.

Tools and Platforms for eFPGA Development

Design and development tools and platforms for eFPGA development are essential for creating efficient and high-performance FPGA designs.

Here is a list of some companies along with their associated eFPGA development tools:

1. Achronix Semiconductor:
   • Tool Suite: ACE (Achronix CAD Environment)
   • Description: ACE provides a comprehensive set of tools for designing and programming Achronix FPGAs and eFPGAs.
2. Flex Logix Technologies:
   • Tool Suite: EFLX Compiler
   • Description: The EFLX Compiler assists in the configuration and optimization of Flex Logix’s eFPGA IP.
3. Menta:
   • Tool Suite: Menta eFPGA Development Kit
   • Description: The eFPGA Development Kit includes tools for design, verification, and integration of Menta’s eFPGA IP.
4. QuickLogic Corporation:
   • Tool Suite: QuickLogic QuickWorks
   • Description: QuickWorks is the development tool suite for QuickLogic’s eFPGA solutions, providing design and programming capabilities.
5. Efabless Corporation:
   • Tool Suite: OpenFPGA Tools
   • Description: Efabless supports an open-source approach to eFPGA development with the OpenFPGA initiative, providing accessible tools for customization.
6. Lattice Semiconductor:
   • Tool Suite: Lattice Diamond
   • Description: While Lattice Semiconductor is more known for standalone FPGAs, their Diamond tool suite may include features for their embedded FPGA solutions.

Comparison eFPGAs with Traditional FPGAs

Contrasting Features

eFPGAs and traditional FPGAs are both types of field-programmable gate arrays, which are integrated circuits that the user can configure to implement various logic functions. However, there are some key differences between them in terms of size, cost, power, and performance. eFPGAs are embedded FPGAs that are available as IP cores that can be integrated into ASICs or SoCs, while traditional FPGAs are standalone chips that have their own I/O and power control circuitry. eFPGAs are smaller and cheaper than traditional FPGAs, as they can be tailored to the exact size, ratio, and quantity of logic, DSP, RAM, and hard blocks that the user needs in any process node. eFPGAs also have lower power consumption and higher performance than traditional FPGAs, as they can leverage the interconnects, clocks, memory, and temperature range of the host SoC or ASIC. Moreover, eFPGAs can be updated post-production without changing the RTL design or re-manufacturing the chips, reducing the cost and time-to-market.

Use Cases for Traditional FPGAs vs. eFPGAs

Traditional FPGAs are discrete chips that can be programmed to implement any logic function. They are widely used for prototyping, accelerating, and customizing digital systems. However, traditional FPGAs have some drawbacks, such as high cost, large area, high power consumption, and complex board design. eFPGAs are embedded FPGA fabrics that can be integrated into ASICs or SoCs as IP blocks that offer the same programmability and flexibility as traditional FPGAs with lower cost, smaller area, lower power, and simpler integration. eFPGAs enable dynamic reconfiguration of communication protocols and algorithms, which is useful for adapting to changing standards, security requirements, and environmental conditions. In addition, they can improve performance and reduce power consumption by colocating the accelerators with the data processing unit (DPU) on the same chip. Moreover, eFPGAs provide high-performance and low-power acceleration for deep learning inference at the edge and support different neural network architectures and models, which can be updated or changed as needed. They also enable flexible and efficient data compression, encryption, and error correction for storage devices and support different storage protocols and interfaces, which can be customized or updated. Finally, eFPGAs enhance the functionality and performance of microcontrollers by adding programmable logic and memory and enable post-fabrication changes and updates to the microcontroller firmware, extending the product life cycle and reducing the time to market.

Future Trends in eFPGA

The rapid adoption of eFPGA can be attributed to its versatility across various markets and applications. In the defense sector, DARPA has integrated eFPGA into chip development, extending its availability to research teams. Networking firms employ eFPGA to colocate accelerators with data processing units (DPUs) on the same chip, leading to cost savings, lower system power, and enhanced performance. Wireless communication companies leverage eFPGA to enhance the flexibility of their base stations, facilitating customization and real-time updates of protocols and algorithms. Aerospace enterprises, including Boeing, utilize eFPGA to downsize aerospace systems, making them lighter and more power-efficient. The applications extend to storage, AI, and MCU-IoT, encompassing a broad range of use cases that benefit from the flexibility, power efficiency, and cost reduction offered by eFPGA.

As discussed, eFPGA has reached a level of maturity that has led to its widespread adoption in the industry. Its versatility, capacity to support increased computational parallelism, and the option for end-user customization after the silicon stage are now considered essential requirements for companies involved in systems development. the demand for reconfigurability in SoCs is expected to grow, especially given the escalating costs associated with developing SoCs, particularly at advanced process nodes. In order to be cost-effective, providers of SoCs must generate substantial revenue, and designers can achieve this by incorporating reconfigurability, enabling the SoC to be utilized across a broader spectrum of applications.

Conclusions

The following information is inferred on eFPGAs from this article.

eFPGAs are programmable logic units that are embedded directly into larger semiconductor devices, such as SoCs or ASICs. eFPGAs are unique in that they are flexible and versatile. They are appropriate for different industries because they enable designers to create unique digital circuits and accelerators suited to certain applications. One important benefit of eFPGAs is their reconfigurable nature. It makes it possible to program and reprogram the devices, which makes it easier to update, customize, and adjust to changing standards without modifying the hardware. Hardware acceleration activities are where eFPGAs thrive, giving computationally expensive operations a performance boost. eFPGAs can do numerous operations at once because they are designed to support parallel processing. Applications that benefit from parallel computing can process data more quickly because of this parallelism. Applications for eFPGAs can be found in many different industries, including data centers, medical devices, automotive, aerospace, and telecommunications. Because of their versatility, they are useful in situations where quick prototyping and personalization are crucial. These integrated systems’ adaptability is increased by eFPGAs.