Accelerating AI at the Edge: FPGAs for Low-Latency Inference#

Introduction#

Artificial Intelligence (AI) has transformed how we solve complex problems, from image classification and speech recognition to autonomous driving and predictive maintenance. However, the sheer growth in data generation and the need to perform real-time inference in environments with limited network connectivity present unique challenges. These challenges have given rise to “edge computing,” where data processing and inference happen as close to the data source as possible, minimizing latency and resource usage in centralized data centers.

In edge computing scenarios, the demand for low-latency and energy-efficient inference can be stringent. Traditional CPU or GPU solutions may not always be best suited for the edge due to power constraints, size limitations, or performance inefficiencies for certain workloads. Here is where Field-Programmable Gate Arrays (FPGAs) step in as highly customizable hardware solutions that can handle specialized tasks with great efficiency. FPGAs offer:

1. Parallel execution of operations.
2. Reconfigurability for different AI models.
3. Low-latency performance crucial for time-sensitive tasks.
4. Energy efficiency when compared to some high-end CPUs or GPUs.

This blog post explores how FPGAs can accelerate AI workloads at the edge with low latency. We will start with basic concepts, then gradually move to advanced topics like hardware-accelerated neural networks, quantization strategies, design flows, partial reconfiguration, and more. By the end, you will understand why FPGAs represent a powerful solution for edge-based AI and how to start using them in real-world applications.

Table of Contents#

1. What Is Low-Latency Inference?
2. Why Deploy AI at the Edge?
3. FPGA Fundamentals
4. FPGA vs. CPU vs. GPU
5. High-Level Design Flow for FPGA AI Accelerators
6. Basic FPGA Acceleration Example
7. Advanced Concepts
   • Quantization and Model Optimization
   • Pipelining and Parallelism
   • Memory Considerations
8. Getting Started with FPGA Development Environments
9. Example: Simple HLS Code for a Convolution
10. Industry Use Cases
11. Professional-Level Expansions
    • Partial Reconfiguration
    • Dynamic Precision and Reconfigurability
    • Elastic Scaling in FPGA Clusters
12. Conclusion

What Is Low-Latency Inference?#

Low-latency inference refers to the ability of a system to process inputs and generate outputs (inferences) with minimal delay. While “throughput” measures how many inferences per second can be handled, “latency” measures how quickly each inference is computed from the time the input is ready. Low latency is critical in real-time systems, such as:

• Autonomous vehicles reacting to their environment.
• Medical devices interpreting patient data.
• Industrial robots on fast-moving assembly lines.

Achieving low latency is often a function of both the hardware and software design. On the hardware side, specialized accelerators (such as FPGAs) can be configured to handle specific AI workloads very efficiently.

Why Deploy AI at the Edge?#

Deploying AI at the edge is often motivated by challenges in transmitting data to distant data centers, potential privacy concerns, and the need for real-time responsiveness. Here are a few important reasons:

1. Reduced Latency: Edge devices can make quicker decisions without waiting for round-trip communication to a cloud server.
2. Bandwidth Savings: Only essential insights are transmitted over the network instead of raw data, reducing bandwidth usage.
3. Privacy and Security: Processing data locally can help safeguard sensitive information.
4. Energy Efficiency: Edge devices can be optimized for specialized tasks, potentially using less energy than large server deployments for the same tasks.

FPGAs fit into this paradigm because they can be tailored to specific workloads, conserve energy by turning off unused logic, and handle data in real-time.

FPGA Fundamentals#

FPGA vs. CPU vs. GPU#

Below is a simplified comparison table highlighting why FPGAs may outperform CPUs or GPUs in some edge AI scenarios. Note that actual performance depends heavily on workload, design, and constraints.

Key Takeaways:

• FPGAs can be highly optimized to achieve low latency.
• Development complexity is higher, but modern tools (High-Level Synthesis, specialized compilers) simplify the process.
• If your application needs real-time inference, reconfigurability, and energy efficiency, FPGAs might be the best fit.

High-Level Design Flow for FPGA AI Accelerators#

Design flow for FPGA-based AI acceleration can often be summarized in the following steps:

1. Algorithm and Model Development

   • Design or select a neural network architecture (e.g., CNN, RNN, Transformer).
   • Train and validate the model in a framework like TensorFlow or PyTorch.

2. Model Optimization

   • Quantize or prune the model for efficient FPGA deployment.
   • Possibly use specialized FPGA-friendly layers or constraints to reduce resource usage.

3. High-Level Synthesis (HLS) or HDL Design

   • Convert the model or relevant kernels (e.g., convolutions, matrix multiplications) into a hardware-ready form (Verilog, VHDL, or HLS-based C/C++).
   • Insert pipeline stages or parallelism to reduce latency and utilize FPGA resources effectively.

4. Verification and Simulation

   • Use simulation tools to verify correctness and approximate performance.
   • Some frameworks provide reference designs or test benches to help.

5. FPGA Synthesis and Implementation

   • Synthesize your code into a netlist.
   • Map and place-and-route to generate the final FPGA bitstream.

6. Deployment

   • Load the bitstream onto the FPGA device.
   • Integrate with other system components (operating systems, sensors, host CPU, etc.).

7. Testing and Iteration

   • Test performance under real-world conditions.
   • Iterate on the design, reconfiguring the FPGA if needed to refine performance or address new use cases.

Basic FPGA Acceleration Example#

To illustrate a simple concept, consider an FPGA-based accelerator for a single-layer perceptron. While single-layer perceptrons are no longer the cutting edge of AI, they are simple enough to demonstrate fundamental principles.

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module perceptron #(
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    parameter N = 8,    // Number of inputs
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    parameter WIDTH = 8 // Bit-width of each input
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) (
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    input  wire [N*WIDTH-1:0] in_features,
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    input  wire [N*WIDTH-1:0] weights,
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    input  wire [WIDTH-1:0]   bias,
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    output reg  [WIDTH-1:0]   out
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);
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    integer i;
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    reg signed [WIDTH-1:0] feature_val;
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    reg signed [WIDTH-1:0] weight_val;
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    reg signed [2*WIDTH-1:0] mul_result; // extended width
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    reg signed [2*WIDTH-1:0] accum;
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    always @(*) begin
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        accum = 0;
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        for (i = 0; i < N; i = i + 1) begin
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            feature_val = in_features[(i*WIDTH) +: WIDTH];
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            weight_val  = weights[(i*WIDTH) +: WIDTH];
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            mul_result  = feature_val * weight_val;
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            accum       = accum + mul_result;
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        end
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        accum = accum + bias;
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        // Simple activation - threshold at 0
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        if (accum[2*WIDTH-1] == 1'b1) begin
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           // If negative, output zero for demonstration
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           out = 0;
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        end else begin
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           out = accum[WIDTH-1:0]; // Truncate or saturate
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        end
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    end
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endmodule

Advanced Concepts#

Getting Started with FPGA Development Environments#

Modern FPGA toolchains provide myriad ways to implement AI accelerators:

1. Xilinx Vitis AI: A development stack for AI inference on Xilinx devices. Integrates optimizations for quantized models, offers pre-built accelerators, and speeds up FPGA implementations of popular networks.
2. Intel OpenVINO with FPGA Plugins: Intel’s ecosystem for AI acceleration can target CPUS, GPUs, and FPGAs.
3. High-Level Synthesis (HLS) Tools: Tools like Xilinx Vitis HLS or Intel HLS let you describe hardware in C/C++ and automatically generate HDL code.
4. OpenCL: Some vendors offer OpenCL-based workflows for writing kernels that run on the FPGA, with hardware blocks automatically generated.

Example: Simple HLS Code for a Convolution#

Below is a moderately simplified High-Level Synthesis code sample in C that demonstrates a 2D convolution kernel. This code can be synthesized to RTL by tools like Xilinx Vitis HLS or Intel HLS.

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#include <hls_stream.h>
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#include <ap_int.h>
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#define IMG_WIDTH  64
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#define IMG_HEIGHT 64
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#define KERNEL_SIZE 3
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#define BIT_WIDTH 16
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void conv2d_hls(
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    ap_int<BIT_WIDTH> input[IMG_HEIGHT][IMG_WIDTH],
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    ap_int<BIT_WIDTH> kernel[KERNEL_SIZE][KERNEL_SIZE],
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    ap_int<BIT_WIDTH> output[IMG_HEIGHT - KERNEL_SIZE + 1][IMG_WIDTH - KERNEL_SIZE + 1]
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) {
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#pragma HLS PIPELINE II=1
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    for(int row = 0; row < IMG_HEIGHT - KERNEL_SIZE + 1; row++) {
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        for(int col = 0; col < IMG_WIDTH - KERNEL_SIZE + 1; col++) {
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            ap_int<2*BIT_WIDTH> sum = 0;
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            for(int kr = 0; kr < KERNEL_SIZE; kr++) {
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                for(int kc = 0; kc < KERNEL_SIZE; kc++) {
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                    ap_int<BIT_WIDTH> pixel  = input[row + kr][col + kc];
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                    ap_int<BIT_WIDTH> weight = kernel[kr][kc];
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                    sum += pixel * weight;
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                }
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            }
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            output[row][col] = sum; // Potentially apply activation or truncation
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        }
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    }
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}

Industry Use Cases#

1. Autonomous Vehicles: Low-latency object detection is necessary for safety. FPGAs can run lightweight CNNs or specialized detection networks directly inside vehicles.
2. IoT Sensors: Environment monitoring sensors can include an FPGA for analyzing data in real-time, enabling immediate responses.
3. Healthcare: Portable medical devices can perform analysis locally—e.g., ECG pattern recognition—in real time at low power.
4. Industrial Automation: Robotics systems can integrate FPGA-based AI for fast, deterministic control loops.

Professional-Level Expansions#

Once you have mastered the basics of designing AI accelerators on an FPGA, you may want to explore the following advanced topics to push the performance envelope or adapt to evolving requirements.

Conclusion#

FPGAs stand out as an exceptional solution for low-latency, high-performance AI inference at the edge. By tailoring hardware to specific neural network topologies, leveraging parallelism, and exploiting partial reconfiguration, developers can unlock efficiency levels and performance that are difficult to match with traditional CPU or GPU-based solutions. While FPGA design can be more involved, modern tools and platforms significantly shorten the learning curve.

If your application demands real-time decision-making, minimal energy consumption, and the flexibility to adapt to changing AI models, exploring FPGA accelerators should be a top consideration. From basic single-layer perceptrons to advanced multi-layer, quantized networks, FPGAs can be a cornerstone technology for next-generation intelligent devices.