This post was writen by chatGPT and Claude. By interchanging the strong suite each had. I was able to make up a blog on why FPGA lost to the AI race. Its a really intresting perspective. I was honestly amazed by it. Though it is a boring read, its an interesting one.
Also, if you aren’t intrested in the article below — — which is quite boring. You can watch the Mohammed S. AbdelFattah, a previous intel engineer that led the Deep Learning on FPGA effort. Link here
The Initial Appeal of FPGAs for AI Workloads
In the early days of deep learning acceleration, Field-Programmable Gate Arrays (FPGAs) represented a promising solution for AI computation. Their fundamental architecture — consisting of configurable logic blocks (CLBs), digital signal processors (DSPs), and block RAM (BRAM) interconnected through a programmable routing fabric — offered several theoretical advantages:
The ability to implement custom data paths meant that AI practitioners could design processing elements specifically optimized for neural network operations. For example, implementing a systolic array architecture for matrix multiplication could achieve high computational density with minimal off-chip memory access. Microsoft’s Project Brainwave demonstrated this by creating a spatial neural processing architecture that could sustain performance of 39.5 TeraOps at batch size 11 — remarkable for real-time AI inference in 2017.
FPGAs excelled in creating hardware pipelines with deterministic latency, crucial for applications like high-frequency trading where predictable response times measured in microseconds were essential. The absence of an operating system or scheduler meant that execution timing was entirely deterministic, unlike in CPU/GPU environments where context switching and kernel scheduling introduced variability.
Fine-grained parallelism at the bit level allowed designers to choose custom numerical precision tailored to specific algorithms. While GPUs were initially constrained to 32-bit or 16-bit operations, FPGA implementations could use arbitrary bit widths (e.g., 6-bit, 4-bit, or even binary neural networks), potentially fitting more computation into the same silicon area.
The Architectural Limitations That Held FPGAs Back
Despite their theoretical advantages, FPGAs faced fundamental architectural constraints that severely limited their competitiveness in mainstream AI workloads. At the heart of FPGA design lies the programmable interconnect fabric — a complex mesh of routing resources that enables post-manufacturing reconfigurability. This very feature that grants FPGAs their flexibility became their Achilles’ heel in the AI acceleration race. The routing infrastructure consumed approximately 70−90% of the total silicon area in most FPGA architectures, leaving relatively little room for actual computational elements. This vast interconnect network introduced significant signal propagation delays as data traversed multiple switch boxes and connection blocks, creating a fundamental ceiling on achievable clock frequencies.
While custom ASICs and GPUs routinely operated at 1−2 GHz, FPGAs typically struggled to achieve stable designs beyond 300−400 MHz due to these routing constraints. Even Xilinx UltraScale+ and Intel Stratix 10 devices, representing the pinnacle of FPGA technology in 2017, faced timing closure difficulties above 500 MHz for complex designs. This frequency disadvantage meant that to match GPU performance, FPGAs needed to implement dramatically more parallel computational units — a challenge that became increasingly difficult as designs scaled up and routing congestion intensified.
The computational backbone of FPGAs consisted primarily of Digital Signal Processing (DSP) slices — hardened blocks designed to efficiently perform multiply-accumulate operations. However, these resources were relatively scarce compared to the needs of modern deep learning workloads. A high-end FPGA like the Xilinx Virtex UltraScale+ VU9P contained approximately 6,840 DSP48E2 slices, each capable of performing a single 27×18-bit multiply-accumulate operation per cycle. When implementing deep neural networks requiring billions of operations per second, this resource constraint forced designers to time-multiplex DSP blocks, reducing effective throughput and increasing design complexity.
Memory architecture represented another significant limitation for FPGA-based AI acceleration. While FPGAs featured distributed Block RAM (BRAM) resources — small memory blocks distributed throughout the programmable fabric — the total on-chip memory capacity typically ranged from just 20−50 MB in high-end devices. Modern neural networks with millions of parameters quickly exhausted this limited on-chip storage, forcing frequent external memory accesses. Though some premium FPGA models eventually incorporated High Bandwidth Memory (HBM), this integration lagged years behind similar implementations in the GPU ecosystem. The Xilinx Alveo U280, one of the first HBM-equipped FPGAs, which was released in late in 2018, compared to its rival NVIDIA that had already shipped HBM-equipped Tesla P100 GPUs back in 2016.
Perhaps the most consequential limitation lay in the development methodology required for FPGA implementation. Creating optimized FPGA designs demanded expertise in hardware description languages, understanding of digital circuit design principles, and familiarity with concepts like pipelining, retiming, and resource sharing. Hardware compilation flows for FPGAs involved synthesis, place-and-route, and timing analysis — processes that frequently took hours even for incremental changes. While High-Level Synthesis (HLS) tools like Xilinx Vivado HLS and Intel HLS Compiler improved accessibility, they still required substantial hardware design knowledge to achieve competitive performance. The mapping from high-level algorithmic descriptions to efficient FPGA implementations remained far from automatic, often requiring manual intervention to reach performance targets.
This development complexity created a prohibitive barrier for AI researchers and developers accustomed to software-oriented workflows. The iteration cycle for FPGA development — measure, modify, recompile, test — typically spanned hours or days, compared to minutes or seconds for GPU-based development. In a field advancing as rapidly as deep learning, this difference in development velocity proved decisive. Research teams leveraging GPUs could explore dozens of architectural variations and hyperparameter settings in the time it took FPGA-based approaches to evaluate a single design point.
The combination of these architectural limitations — routing overhead restricting clock frequencies, limited DSP resources constraining computational density, insufficient on-chip memory capacity, and prohibitive development complexity — created a perfect storm that prevented FPGAs from keeping pace with the specialized tensor processing capabilities When NVIDIA introduced with its Volta architecture, while FPGAs maintained advantages in certain specific application contexts, their fundamental architecture proved ill-suited to the general-purpose, high-throughput matrix computation demands of modern deep learning workloads.
NVIDIA’s Technical Breakthrough: Tensor Cores and Unified Architecture
But what made this the final defeat of carthage was the the introduction of the Volta architecture in 2017 by NVIDIA which represented a watershed moment in AI acceleration. The V100 GPU, Volta’s flagship implementation, embodied a radical architectural shift that addressed the precise computational patterns emerging in modern neural networks. At the heart of this revolution lay the Tensor Core — a specialized matrix processing unit that departed significantly from traditional GPU computational models. Rather than relying solely on general-purpose FP32 and FP64 arithmetic units, NVIDIA engineers recognized that deep learning workloads exhibited highly structured computational patterns dominated by matrix multiplication operations, particularly in the form
D=A×B+CEach Tensor Core implemented a dedicated circuit path optimized for 4×4 matrix multiplication and accumulation, performing this operation in a single clock cycle. The internal microarchitecture employed a carefully arranged array of multipliers feeding into an adder tree, with specific data paths designed to minimize internal data movement. This design yielded 64 floating-point operations per clock cycle per Tensor Core (16 multiplies and 16 additions, each operating on 16-bit inputs with 32-bit accumulation). With 640 Tensor Cores distributed across 80 Streaming Multiprocessors (SMs) in the V100, and operating at clock frequencies of 1.3−1.5 GHz, the theoretical peak performance reached an unprecedented 125 TFLOPS for mixed-precision computation.
This specialized approach carried inherent trade-offs in flexibility, yet NVIDIA made a calculated gamble that the benefit of orders-of-magnitude higher throughput for matrix operations would outweigh the limitations in programmability. The mixed-precision computational model (FP16 multiplication with FP32 accumulation) aligned perfectly with the numerical requirements of deep learning training, where reduced precision in certain operations had minimal impact on model accuracy. NVIDIA complemented this with sophisticated techniques like loss scaling to preserve numerical stability during backpropagation, effectively mitigating the potential downsides of reduced precision.
Perhaps equally important as the Tensor Core itself was NVIDIA’s holistic approach to memory architecture. Deep learning workloads present challenging memory access patterns, with working sets that frequently exceed on-chip storage capacities. NVIDIA addressed this challenge through a carefully designed memory hierarchy specifically tailored to support tensor operations. The V100 implemented a massive register file (256KB per SM, totaling over 20MB across the chip) that allowed intermediate results to remain on-chip during computation. Each SM also featured 96KB of configurable shared memory/L1 cache that enabled collaborative data sharing across thread groups, crucial for efficiently implementing tiled matrix multiplication algorithms.
The inclusion of a substantial 6MB L2 cache served as a critical bandwidth amplifier, capturing temporal and spatial locality in memory access patterns and significantly reducing external memory traffic. At the highest level of the memory hierarchy, NVIDIA integrated 16GB of High Bandwidth Memory 2 (HBM2) with an aggregate bandwidth of 900 GB/s. This massive memory bandwidth—nearly 3x what contemporary FPGAs could achieve—ensured that the computational units remained adequately fed with data, preventing memory bottlenecks from dominating performance. The entire memory system was designed with consideration for the specific access patterns of convolution and matrix multiplication operations, with hardware prefetchers optimized for the strided memory access patterns common in deep learning workloads.
NVIDIA’s architectural innovation extended beyond raw computational capabilities to encompass the programming model itself. The company developed specialized libraries (cuDNN, cuBLAS, and TensorRT) that presented high-level interfaces to deep learning frameworks while internally leveraging sophisticated algorithms to maximize Tensor Core utilization. These libraries employed techniques like Winograd transformations for convolution, advanced matrix multiplication algorithms, and operation fusion to minimize data movement and maximize computational efficiency. The performance-critical kernels underwent extensive hand-optimization using intrinsic functions and assembly code to extract maximum performance from the underlying hardware.
The CUDA programming model provided a unified framework that abstracted the complexity of the GPU architecture, allowing developers to express parallelism without managing hardware-specific details. Framework integration through TensorFlow, PyTorch, and MXNet meant that researchers could benefit from Tensor Core acceleration without modifying existing code — a stark contrast to the FPGA development experience. NVIDIA’s compiler technology automatically identified opportunities to map operations onto Tensor Cores, with subsequent generations introducing features like automatic mixed precision that further simplified adoption.
The integration of tensor-specific computation with a mature software ecosystem created a virtuous cycle: the performance advantage attracted more developers, whose feedback informed subsequent hardware iterations. NVIDIA leveraged this continuous feedback loop to refine the Tensor Core architecture in later generations. The Turing architecture introduced INT8 and INT4 precision support for inference workloads. Ampere expanded the Tensor Core’s capabilities to handle sparse matrices and added support for FP64 operations. The Hopper architecture’s fourth-generation Tensor Cores incorporated dedicated dataflows for transformer attention mechanisms, directly addressing the computational patterns of large language models.
This evolutionary trajectory demonstrated NVIDIA’s commitment to co-evolving hardware architecture with the rapidly advancing field of deep learning. Unlike FPGA solutions, which required extensive redesign for each new algorithmic pattern, NVIDIA’s unified architecture provided consistent performance improvements across diverse workloads through a combination of specialized hardware and sophisticated software. The Tensor Core represented not merely a computational accelerator but the centerpiece of an integrated ecosystem spanning silicon, systems, and software — a comprehensive approach that fundamentally altered the dynamics of AI hardware acceleration.
Alternative Domains to be explored by FPGAs
Despite GPUs’ dominance, FPGAs maintain advantages in specific technical domains:
- Signal Processing Front-Ends: FPGAs excel at interfacing with high-speed sensors and custom I/O protocols, making them valuable for preprocessing data before sending it to GPU-accelerated AI systems.
- Ultra-Low Latency Systems: For applications requiring deterministic response times below 100 microseconds, FPGAs can still outperform GPUs by eliminating kernel launch overhead and scheduler variability.
- Power-Constrained Edge Deployment: In settings with strict power budgets (1–5W), FPGA implementations can sometimes achieve better efficiency by eliminating unnecessary hardware and precisely matching numerical precision to application requirements.
Conclusion
The displacement of FPGAs by NVIDIA GPUs in mainstream AI workloads illustrates how purpose-built architectures with software-hardware co-design can outperform more flexible but less specialized platforms. NVIDIA’s Tensor Core architecture succeeded by identifying the fundamental patterns in deep learning computation and creating both hardware optimized for these patterns and software that made this optimization accessible to ordinary developers.
While FPGAs continue to evolve and find valuable niches in the AI ecosystem, NVIDIA’s comprehensive approach to the technical challenges of AI computation — spanning from silicon design to developer tools — established a competitive advantage that proved decisive in shaping the modern AI hardware landscape.
