In this talk, we will explore the ways of implementing convolutional neural network (CNN) layers on hardware platforms. As deep learning continues to drive advancements in various fields, the need for efficient and high-performance hardware implementations becomes critical. We will delve into the architectural considerations, including data flow, parallel processing, and memory optimization, necessary for translating CNNs from software to hardware.

Starting with an overview of CNN operations, we will discuss fixed-point arithmetic and its advantages for hardware efficiency. We will then demonstrate a practical example of implementing a CNN layer on an FPGA, highlighting the steps from algorithmic design to hardware synthesis and deployment.

The talk will also cover optimization techniques to enhance throughput and reduce latency, such as parallelism and pipelining. Real-world case studies will illustrate the performance gains and energy efficiency improvements achieved through hardware acceleration of CNNs. By the end of the session, participants will have a comprehensive understanding of the challenges and solutions in implementing CNN layers on hardware, equipping them with the knowledge to embark on their own hardware acceleration projects.

This guide was created with the help of AI, based on the presentation's transcript. Its goal is to give you useful context and background so you can get the most out of the session.

What this presentation is about and why it matters

This talk walks through how 2D image filters and convolutional neural network (CNN) layers are implemented on hardware, with an emphasis on practical techniques used on resource-constrained platforms such as FPGAs and embedded systems. For engineers in signal processing and computer vision, the talk connects algorithmic ideas (convolution, feature extraction) to hardware realities (memory bandwidth, multiplier count, latency and power). Implementing CNNs efficiently on hardware matters because many real-world applications—real-time video analytics, autonomous vehicles, medical imaging, and battery-powered IoT devices—require high throughput, low latency, and low energy per inference. A well-designed hardware implementation can turn a theoretically accurate model into a practical, deployable system.

Who will benefit the most from this presentation

• DSP engineers and system architects who need to move image-processing workloads from CPU/GPU to FPGA/ASIC.
• Embedded systems developers building real-time vision pipelines on edge devices.
• Graduate students and researchers learning how CNN operations map to hardware resources.
• Hardware design engineers (FPGA/RTL) who want a practical refresher on buffers, dataflow, and arithmetic trade-offs.
• Machine learning practitioners curious about what changes when a model leaves the software environment for silicon.

What you need to know

The talk assumes familiarity with basic digital signal processing and linear algebra. The following concepts will help you get the most out of the presentation:

• 2D convolution: The fundamental operation for image filters and CNN layers. For a single-channel image and K×K kernel, each output pixel is the sum of element-wise products across the K×K patch. In compact form: $y[i,j]=\sum_{m=0}^{K-1}\sum_{n=0}^{K-1} x[i+m,j+n]\;w[m,n]$. For multi-channel inputs with C_in channels and multiple filters, the per-output-channel formula is $y[i,j,c_{out}]=\sum_{c_{in}=0}^{C_{in}-1}\sum_{m}\sum_{n} x[i+m,j+n,c_{in}]\;w[m,n,c_{in},c_{out}]$.
• Kernels / Filters: Small matrices whose coefficients (weights) define the operation (edge detection, blur, learned features in CNNs). In CNNs these are learned during training; on hardware they become fixed or quantized weights for inference.
• Padding and stride: Padding (often zero-padding) handles edges while preserving output size; stride controls how far the kernel jumps and affects output resolution and compute.
• Line buffers and data reuse: Reading overlapping patches naively causes many redundant memory accesses. Line buffers store a few image rows to supply sliding windows with minimal memory reads—critical for throughput on hardware.
• Multiply-accumulate (MAC): The inner loop of convolution. Hardware implementations trade off the number of parallel MAC units (area and power) against latency and throughput.
• Fixed-point arithmetic: Converting floating-point models to fixed-point reduces area and power but needs care (dynamic range, quantization noise, scaling factors).
• Partial sums and tiling: Large channel counts or many filters are handled by computing partial sums over subsets of channels or filters and accumulating results across passes to reduce instantaneous hardware resource needs.
• Parallelism and pipelining: Two main axes to improve performance—spatial parallelism (more MAC units) and temporal pipelining (overlap operations across cycles).
• Trade-offs: Key design parameters include multiplier count, memory bandwidth, on-chip buffer size, clock frequency, and power. The presentation shows how these interact for practical FPGA implementations.

Glossary

• Convolution: A sliding-window multiply-and-sum operation applied to an image patch and kernel to produce filtered output.
• Kernel / Filter: A small matrix of coefficients applied to an image patch; in CNNs, these are learned weights.
• Feature map: A single channel of an image or intermediate CNN layer; inputs are input feature maps, outputs are output feature maps.
• Line buffer: On-chip memory that stores a few rows of the input image to provide sliding-window patches without rereading main memory.
• MAC (Multiply-Accumulate): The unit that multiplies inputs by weights and accumulates the sum; the core of convolution hardware.
• Fixed-point arithmetic: Numeric representation with fixed fractional bits used to reduce hardware cost compared to floating point.
• Partial sums (tiling): Breaking channel or filter loops into smaller chunks, computing partial results, and accumulating them to reduce peak resource usage.
• Stride: The step size of the kernel movement across the input; larger strides reduce output resolution and computation.
• Padding: Adding virtual border pixels (usually zeros) so output size can be preserved after convolution.
• FPGA: Field-Programmable Gate Array, a reconfigurable hardware platform commonly used for prototyping and deploying accelerated CNN inference.

Final notes

This presentation provides a clear, practical bridge between the mathematical view of convolutional operations and the engineering choices required to deploy them on hardware. The speaker’s step-by-step treatment—from simple 2D filters to multi-channel CNN layers, and from naive memory access to line buffers and partial sums—gives listeners actionable patterns to apply on FPGAs or custom accelerators. If you are designing or optimizing vision pipelines for real-world devices, this session is a concise and useful primer that will help you ask the right questions and avoid common pitfalls. Enjoy the talk—there are many small, practical ideas here that add up to big gains in throughput and efficiency.