Field-Programmable Neural Networks

• Field-programmable neural network architectures are reconfigurable hardware designs that dynamically map AI models onto programmable logic for tailored performance.
• They support diverse implementations ranging from FPGA-based digital systems to analog, photonic, and quantum platforms with flexible topology and parameters.
• They leverage algorithm–hardware co-design and dynamic reconfiguration techniques to achieve strict latency, throughput, and energy-efficiency targets in real-world applications.

A field-programmable neural network architecture is a class of hardware design that enables on-demand, reconfigurable deployment of neural network models—mapping and adapting their structure, weights, and computational resources directly into programmable logic or specialized hardware, with the goal of providing rapid, energy-efficient inference or learning. Such architectures span FPGA-based digital realizations, mixed-signal and analog implementations, memory-centric processing, emerging photonic and quantum models, and extend to new algorithm-hardware co-design paradigms. The defining property is dynamic configurability: the ability to alter the neural model mapped to hardware—including topology, precision, connectivity, and function—using either hardware or software automation. This capability enables accelerators to adapt to task requirements, resource constraints, or real-time deployment needs, while often delivering strict latency, throughput, and power targets unachievable with fixed hardware.

1. Principles of Programmability and Reconfiguration

Programmability in neural network hardware is realized primarily along two axes: architectural/topological reconfiguration and parametric/programmatic adjustment.

• Architectural/topological reconfiguration includes the ability to program the number of layers, neurons, kernel sizes, or more generally, the structural connectivity of the neural network. In digital systems (e.g., FPGAs), this is typically achieved via soft hardware modules (e.g., reconfigurable RAM, LUTs, MAC arrays, switch fabrics) whose connectivity can be reprogrammed at runtime or design time (Hao, 2017, Dey et al., 2018, Ji et al., 2019). Emerging approaches include programmable photonic meshes or quantum registers (Becker et al., 2023, Silva et al., 2016).
• Parametric adjustment refers to the (re)programming of weights, biases, and activation function parameters. In many FPGA-based and ReRAM-based accelerators, the weights are loaded post-deployment or even adjusted online through learning, exploiting embedded memories, on-chip ReRAM, or distributed memories (Wang et al., 2015, Ji et al., 2019, Ankit et al., 2019).
• Control via software or algorithmic toolchains: Full-stack programmable solutions include software systems that automate neural network to hardware mapping, manage quantization, schedule tasks, and balance resource allocations (e.g., neural synthesizers, graph mappers, schedulers, and placement/routing tools) (Ji et al., 2019, Jokic et al., 2021, Jiang et al., 2019).
• Hybrid and analog programmability: Recent work extends programmable principles to mixed-signal (analog+digital) and even fully analog systems, leveraging reconfigurable interconnects, programmable synaptic weights (e.g., with NVMs or memristors), or versatile crossbars (Yin et al., 3 Aug 2024, Ankit et al., 2019, Duran et al., 22 Sep 2025).

2. Hardware Realization: Digital, Mixed-signal, and Beyond

Field-programmable neural network architectures are realized using diverse hardware substrates, each with distinct programmability patterns and constraints.

FPGA-based Architectures: The majority of field-programmable neural network systems exploit the logic, DSP, BRAM, and flexible routing networks of FPGAs to instantiate neural layers, synaptic weights, and activation functions. Designs include:

• Deep pipelined and modular architectures with parameterizable blocks for multi-layer DNNs and support for flexible training/inference workloads (Hao, 2017, Yi et al., 2021).
• Sparse architectures with tunable connectivity and degrees of parallelism for resource/speed trade-offs (Dey et al., 2018).
• Look-up table (LUT) based architectures embedding sub-networks for fast inference (Andronic et al., 29 Feb 2024).

Processing-in-Memory and Emerging Memory Substrates: ReRAM-based crossbars allow simultaneous storage and computation of weights, supporting massively parallel (and field-programmable) vector-matrix multiplications. Architectures systematically expose programmability via:

• On-chip programming of synaptic strengths in crossbars (Ji et al., 2019, Ankit et al., 2019).
• Software stacks for automated mapping, scheduling, and configuration of neural operations (Ji et al., 2019).
• Bit-slicing techniques to improve precision and support for different learning algorithms (Ankit et al., 2019).

Analog and Mixed-Signal Implementations: Compact analog neuron and synapse circuits with programmable parameters (such as leaky-integrate-and-fire neurons, adjustable weights via MOS switches, or on-chip digital control for learning and routing) provide field-programmability for neuromorphic and reservoir computing systems (Duran et al., 22 Sep 2025).

Photonic and Quantum Architectures: Programmability is extended to all-optical and quantum domains:

• Programmable photonic-neural mesh with configurable delay, phase, and feedback (e.g., the optoacoustic OREO for recurrent networks) (Becker et al., 2023).
• Quantum perceptron models leveraging unitary operators to encode additive and multiplicative field operations, and superposition-based architecture search for polynomial-time programmable model selection (Silva et al., 2016).

3. Algorithm–Hardware Co-Design and Mapping

Modern field-programmable neural network systems emphasize tight coupling between neural model design and hardware mapping, with co-design flows and automated toolchains:

• Neural Synthesis and Quantization: Software pipelines extract neural topologies from frameworks (e.g., TensorFlow, Caffe), perform quantization-aware optimization, and synthesize reconfigurable hardware macros (e.g., MAC blocks, LUTs, activation LUTs) with user-specified precisions (Jokic et al., 2021, Jwa et al., 2022, Andronic et al., 29 Feb 2024).
• Task Scheduling and Pipelining: Schedulers examine data dependencies, tile-based partitions, tiling strategies, and pipeline balancing to optimize for throughput, latency, and resource allocation (Jiang et al., 2019, Yi et al., 2021, Franca-Neto, 2018).
• Resource-Balanced Streaming: Systems compute per-layer processing rates and auto-balance hardware allocation to prevent pipeline stalls and maximize utilization (Jokic et al., 2021, Yi et al., 2021).
• Field-aware Architecture Search: Hardware-constrained NAS frameworks (e.g., FNAS, PASNet) incorporate hardware cost models, operator latency tables, and resource constraints directly into the architecture search objective, yielding deployable neural topologies guaranteed to meet hardware-specific efficiency and latency budgets (Jiang et al., 2019, Peng et al., 2023).

4. Optimization, Pruning, and Scalability Techniques

Efficiency and scalability are central challenges. Field-programmable architectures use the following schemes:

• Structured Sparsity and Pruning: Look-ahead kernel pruning (LAKP) and structured sparsity reduce the number of active parameters and computation, controlling both area and energy while preserving accuracy (Rahoof et al., 3 Sep 2025, Dey et al., 2018).
• Parallel Edge Processing: In sparse networks, computations are edge-centric, pipelining FF, BP, and weight updates, and parametrizing parallelism for dynamic resource efficiency (Dey et al., 2018).
• Hierarchical LUT Embedding: Mapping deep, residual, floating-point sub-networks to a single logical LUT hides neural complexity, minimizing circuit depth and inference latency (Andronic et al., 29 Feb 2024).
• Dynamic Reconfiguration: Architectures such as FPCA enable runtime adjustment of kernel size, weight values, stride, and channel count by simply re-programming memory arrays or digital configuration registers (Yin et al., 3 Aug 2024).
• Time-Multiplexing and Core Sharing: Hardware neurons are dynamically reused for virtual neuron emulation, via high-speed time-division multiplexing, dramatically reducing resource footprint (Wang et al., 2015).

5. Learning, Adaptation, and Hardware-Intrinsic Algorithms

While most programmable neural architectures target inference, several designs support on-chip learning and adaptation:

• Online and In-situ Training Algorithms: Weight update rules such as the online pseudoinverse update method (OPIUM) enable online adaptation of output decoding weights in resource-limited hardware (Wang et al., 2015); Recursive Least Squares (FORCE) training is implemented efficiently within analog-digital hybrid SNNs using hardware-friendly update flows (Duran et al., 22 Sep 2025).
• Programmable Activation Functions and Nonlinearity: Compact, hardware-tunable activation modules (e.g., polynomial or trainable activations for cryptography-constrained architectures (Peng et al., 2023), analog nonlinearity compensation models in sensor arrays (Yin et al., 3 Aug 2024)) broaden the expressivity of field-programmable accelerators.
• Quantum and Photonic Learning: Quantum parallelism (simultaneous weight and architecture search) (Silva et al., 2016) and optical recurrent control (OREO) (Becker et al., 2023) enable fully programmable, physically adaptive learning at scales not accessible in classical digital electronics.

6. Performance Benchmarks and Application Domains

Key performance criteria—latency, throughput, energy efficiency, and hardware utilization—are the central motivation for these architectures:

Architecture	Throughput	Latency	Design Platform / Application
NEF on FPGA (Wang et al., 2015)	5.12M/s (theor)	120 μs	Handwriting, speech, images
Sparse NN FPGA (Dey et al., 2018)	~96.5% acc.	0.4–32 μs	MNIST, on-chip training/inference
FPSA ReRAM (Ji et al., 2019)	up to 1000× PRIME	156 ns PE	VGG16, pattern recognition
FastCaps CapsNet FPGA (Rahoof et al., 3 Sep 2025)	up to 1351 FPS	N/A	MNIST, F-MNIST, edge applications
NeuraLUT (Andronic et al., 29 Feb 2024)	12 ns	N/A	MNIST, LHC jet tagging
FPCA (Yin et al., 3 Aug 2024)	N/A	N/A	Pixel-wise real-time vision, edge
IBM INC (Narayanan et al., 2020)	864 GB/s	N/A	Neuroscience/AI prototyping

These results demonstrate the diversity of targets—ranging from sub-10 ns latency on LUT accelerators for "hard real-time" tasks, to multi-million inferences per second in pipelined digital systems, to flexible, analog domain SNNs for energy-constrained edge devices.

7. Implications, Open Challenges, and Future Directions

Field-programmable neural network architectures are accelerating a shift toward domain-specific, adaptable AI computing—reaching real-time performance under severe energy, latency, and resource constraints:

• AI at the Edge and On-Sensor: Integration of neural processing in situ (e.g., FPCA, analog sensor arrays, low-power SNNs) enables privacy-preserving, ultra-low-power AI directly where data are sensed (Yin et al., 3 Aug 2024, Duran et al., 22 Sep 2025).
• Hardware-aware Neural Architecture Search: Inclusion of hardware cost, dataflow, and cryptographic constraints in NAS yields architectures tunable to field hardware under strict energy or security budgets (Jiang et al., 2019, Peng et al., 2023).
• ASIC and Hybrid Architectures: Many techniques originating in FPGA and ReRAM systems (e.g., sub-network LUT mapping, bit-slicing for high precision, dynamic sparsity) inform new ASIC designs and cross-platform solutions.
• Analog and Quantum Programmability: As analog, photonic, and quantum substrates mature, field-programmable neural computing principles provide a blueprint for exploiting unique physical properties (e.g., low-power analog, coherent memory, quantum parallelism) (Becker et al., 2023, Silva et al., 2016).
• Limitations and Trade-Offs: Challenges remain in scalability of on-chip memory for large models, ASIC-LUT area growth for high expressivity, analog circuit non-idealities, and the programmability-security-robustness trilemma in edge deployments.

Field-programmable neural network architectures represent a convergence of AI, hardware, and system design, where configurable computation is exploited for optimal, adaptive, and efficient deployment across application domains—ranging from data centers and experimental science to autonomous, real-time, privacy-preserving edge systems.