Unlocking AI Engine Performance: A New Framework for Neural Network Compilation

Author: Denis Avetisyan

Researchers have developed AIE4ML, a complete system for translating neural networks into efficient instructions for AMD’s next-generation AI Engines.

The compilation pipeline transforms a high-level network into an optimized AIE project through successive refinement stages-including quantization, tiling, packing, and graph connectivity resolution-ultimately yielding a deployable, hardware-ready implementation.

AIE4ML optimizes on-chip dataflow and leverages quantization to achieve high throughput and low-latency inference for FPGA-based neural network acceleration.

Achieving efficient AI inference on emerging hardware architectures demands overcoming challenges in dataflow and memory management. This paper introduces AIE4ML: An End-to-End Framework for Compiling Neural Networks for the Next Generation of AMD AI Engines, a comprehensive solution for automatically converting neural networks into optimized firmware targeting AMD’s AIE-ML devices. By leveraging structured parallelization, quantization, and a novel graph placement algorithm, AIE4ML achieves GPU-class throughput with microsecond latency while maintaining entirely on-chip data movement. Will this framework unlock new possibilities for ultra-low-latency AI applications in fields like particle physics and beyond?

The Inevitable Demand for Specialized Computation

The relentless expansion of deep learning into nearly every facet of technology is driving an unprecedented demand for dedicated artificial intelligence hardware. Initially, many deep learning tasks were executed on general-purpose central processing units, and later, graphics processing units offered a substantial performance boost. However, the increasing complexity of neural networks – characterized by billions of parameters and computationally intensive matrix operations – has begun to overwhelm these conventional architectures. This surge in demand isn’t merely about faster processing; it’s about achieving the energy efficiency necessary for widespread deployment, from cloud-based services to edge devices. Consequently, the industry is witnessing a rapid evolution towards specialized processors, or AI accelerators, engineered to efficiently handle the unique computational demands of deep learning models and unlock their full potential across diverse applications.

Contemporary computing infrastructure, largely built around general-purpose central processing units, increasingly falters when tasked with the demands of modern deep learning models. These models, characterized by billions of parameters and complex matrix operations, require immense computational throughput and memory bandwidth. While capable of performing these calculations, general-purpose processors are not optimized for them, leading to significant inefficiencies – high latency, substantial power consumption, and limited scalability. The core issue stems from the mismatch between the architecture of these processors and the repetitive, parallel nature of deep learning workloads. Consequently, substantial portions of computational resources are often underutilized, hindering the ability to effectively train and deploy increasingly sophisticated artificial intelligence systems. This limitation drives the need for dedicated hardware solutions, specifically designed to overcome these bottlenecks and unlock the full potential of advanced AI.

The escalating demands of deep learning are driving a fundamental change in computing architecture, moving beyond general-purpose processors to AI Accelerators. These specialized circuits are meticulously designed to efficiently handle the core mathematical operations-matrix multiplications, convolutions, and activation functions-that underpin modern neural networks. Unlike CPUs and GPUs, which are versatile but often inefficient for these specific tasks, AI Accelerators prioritize throughput and energy efficiency by streamlining the computational pathways for AI workloads. This optimization isn’t merely about speed; it enables the deployment of increasingly complex models on edge devices and within power-constrained environments, paving the way for real-time applications in areas like autonomous driving, personalized medicine, and advanced robotics. The trend represents a significant investment in hardware tailored to the unique needs of artificial intelligence, promising substantial gains in performance and scalability as models continue to evolve.

The AIE4ML hardware design leverages blocked layer kernels, layer-level scaling with input broadcasting, and cross-layer pipelining through memory tiles to enable efficient, fully on-chip multi-layer execution.

A Versatile Architecture for Adaptable Computation

The Versal architecture utilizes a heterogeneous system-on-chip (SoC) approach, integrating three primary components: AI Engines (AIE), Programmable Logic (PL), and a Processing System (PS). The PS functions as the central control plane, running standard operating systems and managing system resources. The PL provides the flexibility of customizable hardware acceleration via configurable logic blocks, enabling implementation of custom interfaces and specialized functions. Crucially, the AIEs are dedicated hardware accelerators optimized for artificial intelligence and machine learning tasks, operating in parallel with the PS and PL to offload computationally intensive workloads and improve overall system performance. This integration allows for a flexible and adaptable platform capable of supporting a wide range of applications and workloads.

The Versal architecture’s heterogeneous design, integrating AI Engines (AIE), Programmable Logic (PL), and a Processing System (PS), enables adaptability across a wide range of artificial intelligence workloads. This is achieved by allowing developers to partition tasks based on optimal execution characteristics; dataflow-intensive operations like matrix multiplication benefit from the parallel processing capabilities of the AIEs, while more general-purpose or control-oriented functions can be handled by the PS. The PL provides a reconfigurable hardware platform for custom acceleration or interface logic, facilitating optimization for specific AI models and algorithms. This flexibility allows a single Versal device to efficiently support diverse applications, ranging from computer vision and natural language processing to robotics and edge AI.

AI Engines (AIEs) utilize a Very Long Instruction Word (VLIW) architecture to maximize parallelism and throughput for dataflow-intensive computations. Unlike traditional processors operating on control flow, AIEs execute multiple operations simultaneously within a single instruction, enabling high performance in applications where data dependencies are well-defined. This is particularly beneficial for linear algebra operations common in machine learning, such as Generalized Matrix Multiplication (GEMM) – $C = A \times B$ – and Generalized Matrix-Vector Multiplication (GEMV) – $y = A \times x$ – where large volumes of data can be processed concurrently, minimizing data movement and maximizing computational efficiency.

The AIE-ML architecture extends the capabilities of the AI Engines (AIEs) by incorporating significantly larger local memories. These expanded on-chip memories, exceeding the capacity of standard AIE configurations, reduce the need for frequent off-chip data access. This optimization is critical for performance-sensitive applications, particularly those involving large datasets or complex models, as it minimizes data transfer latency and power consumption. The increased local memory capacity enables the AIEs to store and process larger intermediate results and model parameters directly within the compute array, thereby improving throughput and overall system efficiency for dataflow-intensive machine learning tasks.

A single linear layer with bias and ReLU activation efficiently scales across a large array of AIE tiles, achieving 97.4% utilization with 296 tiles of a possible 304.

AIE4ML: A Complete Compilation Pipeline for Efficiency

AIE4ML functions as a complete compilation pipeline, transforming neural network models into executable code for AMD’s Artificial Intelligence Engine – Machine Learning (AIE-ML) devices. This end-to-end process encompasses model loading, optimization, and code generation specifically tailored for the AIE-ML architecture. The framework accepts models defined in standard formats and automatically handles the complexities of mapping the computational graph onto the AIE array, streamlining deployment and enabling efficient execution of neural network inferences on AMD hardware. It provides a unified workflow, reducing the need for manual intervention in the compilation and optimization stages.

AIE4ML integrates with prevalent machine learning frameworks, TensorFlow and PyTorch, to streamline the development process for AMD AIE-ML devices. This support allows developers to utilize existing models and workflows established within these frameworks without requiring substantial code modification or retraining. Specifically, AIE4ML provides tools and interfaces to import models defined in TensorFlow or PyTorch, enabling automated conversion and optimization for deployment on the AIE array. This compatibility reduces the barrier to entry for developers familiar with these ecosystems and accelerates the transition from model training to hardware implementation.

AIE4ML incorporates several optimizations to improve neural network performance on AMD AIE-ML devices. Efficient Linear Layer Implementations minimize computational overhead within fully connected layers, a significant portion of many neural networks. Furthermore, the framework utilizes quantization techniques to reduce the precision of weights and activations – typically from 32-bit floating point to 8-bit integer or lower – thereby decreasing memory bandwidth requirements and accelerating computation. Quantization is performed with minimal loss of accuracy through calibration and training-aware techniques, allowing for a favorable trade-off between performance and model fidelity.

AIE4ML employs an Intermediate Representation (IR) to facilitate optimized compilation of neural network graphs for the AMD AIE-ML device. This IR serves as an abstraction layer, allowing for platform-independent optimizations before generating hardware-specific code. Following IR-based optimizations, the Graph Placement Algorithm is utilized to map the computational graph onto the AIE array. This algorithm considers data dependencies and array topology to minimize data movement and maximize parallelism, thereby improving performance and energy efficiency. The algorithm’s objective is to assign each node in the graph to a specific processing element within the AIE array, optimizing resource utilization and communication pathways.

Using a branch-and-bound algorithm, automatic placement on a 38x8 AIE array results in shorter inter-layer connections and reduced row bias compared to greedy baseline approaches. — Using a branch-and-bound algorithm, automatic placement on a 38×8 AIE array results in shorter inter-layer connections and reduced row bias compared to greedy baseline approaches.

Optimizing Dataflow and Memory Utilization for Peak Performance

Memory Tiles within the AIE-ML architecture function as locally accessible, shared buffer spaces facilitating data exchange between successive layers of a neural network and enabling data redistribution across the AIE array. These tiles are physically implemented as on-chip memory and are partitioned to allow parallel access by multiple AIE cores. Data is transferred into these tiles from external memory, processed by the AIE cores, and then redistributed to other tiles or back to external memory as needed, minimizing off-chip memory bandwidth requirements. The tile architecture supports various data layouts and access patterns to optimize data reuse and maximize throughput during computationally intensive operations like matrix multiplication and convolution.

Double buffering and Run-Time Parameter Loading (RTP) are employed to mitigate the performance bottleneck caused by data transfer between processing elements and memory. Double buffering allows the AIE array to operate on one data tile while simultaneously receiving the next, effectively hiding data transfer latency. RTP facilitates the loading of parameters required for subsequent computations during the execution of the current operation, preventing stalls due to parameter retrieval. This overlap of communication and computation improves overall throughput and utilization of the AIE array by minimizing idle cycles and maximizing the efficiency of data handling within the dataflow graph.

AutoMM and CHARM are kernel composition techniques designed to accelerate General Matrix Multiply (GEMM) operations by distributing computation across both the Array of Integrated Engines (AIE) and the Programmable Logic (PL). AutoMM automatically generates optimized GEMM kernels tailored to the specific AIE array configuration and matrix dimensions, while CHARM facilitates the composition of these AIE-based kernels with PL-based functions for handling data movement and other auxiliary tasks. This cooperative execution on AIE and PL allows for significant performance gains compared to implementations solely on either processor, by leveraging the parallel processing capabilities of the AIE and the flexibility of the PL for offloading tasks such as data prefetching and post-processing. The combination reduces data transfer overhead and maximizes computational throughput for matrix operations.

The Graph Placement Algorithm employs a Branch-and-Bound Search to determine the optimal mapping of computational operations onto the AIE array. This search method systematically explores potential placements, assigning each operation to a specific AIE tile. During exploration, the algorithm calculates a cost function – evaluating factors like data transfer distances and resource contention – to estimate the performance of each mapping. Branches representing mappings exceeding a pre-defined cost threshold are pruned, significantly reducing the search space. The algorithm continues branching and bounding until a mapping with the lowest cost – and therefore the most efficient utilization of the AIE array – is identified and implemented.

Demonstrated Impact and Future Directions in Specialized Computation

The AIE4ML framework distinguishes itself through a versatile architecture capable of efficiently executing a broad spectrum of neural network designs, extending beyond conventional convolutional networks to encompass more recent innovations like MLP-Mixers and models heavily reliant on linear layer implementations. This adaptability stems from a design philosophy prioritizing flexible dataflow and optimized computation graphs, allowing it to readily accommodate the unique characteristics of different network topologies. Consequently, AIE4ML isn’t limited to accelerating specific, pre-defined models; instead, it provides a platform for deploying and optimizing a diverse range of architectures, fostering innovation and enabling the exploration of cutting-edge machine learning techniques without significant hardware constraints. This capability is particularly valuable as the field increasingly moves towards novel architectures that deviate from traditional structures.

The enhanced computational capabilities of AIE4ML extend beyond theoretical benchmarks, directly impacting the performance and energy profiles of increasingly complex artificial intelligence applications. Advancements in computer vision, specifically through architectures like Vision Transformers (ViT), benefit from the framework’s ability to accelerate matrix operations critical to image processing and object detection. Simultaneously, emerging state space models, such as Mamba, which excel in processing sequential data, realize substantial gains in throughput and reduced power consumption. This broad applicability, spanning both image-centric and sequence-based AI, positions AIE4ML as a versatile accelerator capable of handling diverse workloads with improved efficiency and scalability, thereby enabling more sophisticated and sustainable AI solutions.

The AIE4ML framework’s inherent adaptability positions it as a strong candidate for deployment within the high-throughput, low-latency environments characteristic of demanding scientific applications, notably those at CERN. Particle physics experiments, such as those conducted at the Large Hadron Collider, generate massive datasets requiring real-time analysis and event reconstruction. The framework’s ability to efficiently execute diverse neural network architectures, coupled with its high spatial utilization and impressive peak throughput of 113.4 TOPS, enables the acceleration of complex data processing pipelines. This is crucial for tasks like particle identification, track reconstruction, and anomaly detection, where even minor improvements in processing speed can significantly impact the scope and efficiency of ongoing research. Furthermore, the framework’s energy efficiency is a valuable asset, addressing the increasing power demands of large-scale scientific computing facilities.

The AIE4ML framework demonstrably achieves a peak throughput of 113.4 Tera Operations Per Second (TOPS) when processing a 7-layer 512×512 Multi-Layer Perceptron (MLP). This performance benchmark signifies a substantial advancement, as the framework surpasses the capabilities of currently available state-of-the-art Graphics Processing Units (GPUs), Field-Programmable Gate Arrays (FPGAs), and even Apple Neural Engines in similar computational tasks. Such a high throughput is achieved through optimized dataflow and parallel processing capabilities, enabling faster execution of complex neural network models and potentially unlocking new possibilities in areas like real-time image processing and high-frequency data analysis. The result indicates that AIE4ML offers a compelling alternative for accelerating machine learning workloads, particularly where energy efficiency and speed are paramount.

The AIE4ML framework exhibits remarkable efficiency in integer-8 (INT8) computations, attaining 82.2% of the theoretical peak performance on the dedicated AIE-ML device. This high level of performance signifies a substantial optimization in data processing, enabling complex neural networks to operate with minimized computational overhead. Such efficiency is critical for deploying advanced machine learning models in power-constrained environments or applications demanding real-time responsiveness. By maximizing the utilization of INT8 precision, AIE4ML delivers a compelling balance between computational speed and energy consumption, positioning it as a strong contender for accelerating a wide range of artificial intelligence tasks.

The AIE4ML framework exhibits remarkably scalable performance, maintaining consistently high efficiency as computational precision is adjusted. Evaluations demonstrate that the system achieves 97.3%, 98.6%, and 97.1% of peak performance when utilizing i8x8, i16x8, and i16x16 integer precisions, respectively. This sustained efficiency is crucial for deploying complex neural networks, as it allows for a trade-off between computational cost and accuracy without significant performance degradation. The ability to effectively leverage different precision levels not only optimizes resource utilization but also broadens the framework’s applicability to a wider range of hardware platforms and application requirements, demonstrating a robust and adaptable architecture.

The AIE4ML framework exhibits remarkably efficient hardware utilization, achieving 97.4% spatial efficiency on the processing array. This translates to 296 out of 304 available AIE tiles actively engaged in computation, minimizing wasted resources and maximizing throughput. Such high spatial utilization is critical for performance, as it indicates a dense packing of operations onto the hardware, reducing communication overhead and enabling significant acceleration of neural network workloads. This optimized arrangement allows AIE4ML to deliver superior performance compared to conventional architectures, and positions it as a compelling solution for power-constrained and performance-critical applications.

The pursuit of optimized dataflow, as demonstrated by AIE4ML, echoes a fundamental tenet of computational elegance. Ada Lovelace observed, “That brain of man will never rest until it has unveiled the mysteries of the universe.” This sentiment applies directly to the framework’s ambition to maximize throughput and minimize latency within the AIE-ML architecture. The meticulous compilation process, leveraging quantization and on-chip dataflow optimization, isn’t merely about achieving functional results; it is about uncovering the inherent mathematical possibilities within neural networks. The framework embodies a quest for provable efficiency, moving beyond empirical testing toward a deeper understanding of computational limits and potential.

What Lies Ahead?

The presented work, while demonstrating a functional compilation pathway for neural networks onto AMD’s AI Engine, merely scratches the surface of a far deeper challenge. The pursuit of throughput, often lauded as the primary metric, obscures a fundamental truth: architectural efficiency demands provable correctness, not simply empirical performance on benchmark datasets. Future investigations must prioritize formal verification of the compiled dataflow graphs, guaranteeing the absence of subtle errors introduced during quantization or graph transformations. Optimization without analysis remains self-deception, a trap for the unwary engineer.

A significant limitation resides in the implicit assumption of model homogeneity. Real-world deployments increasingly involve dynamic neural networks – models that adapt and evolve. Extending AIE4ML to accommodate such architectures requires a re-evaluation of the static compilation strategy, perhaps toward a more flexible, runtime-reconfigurable approach. The cost, naturally, will be increased complexity, a trade-off demanding rigorous mathematical modeling to ensure predictable performance.

Ultimately, the true test lies not in accelerating existing models, but in enabling the design of novel neural architectures specifically tailored to the constraints and capabilities of the AIE. This necessitates a symbiotic co-design process – a feedback loop between algorithm development and hardware implementation – guided by the principles of information theory and computational complexity. Only then can one hope to transcend the limitations of current, largely ad-hoc, acceleration techniques.

Original article: https://arxiv.org/pdf/2512.15946.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/