SECDA-TFLite Design Toolkit for FPGA DNN Acceleration

• SECDA-TFLite Design Toolkit is a comprehensive methodology and tool suite for co-designing FPGA-based deep neural network accelerators within the TFLite ecosystem.
• It employs a simulation-first approach using SystemC, automated host-accelerator interfacing, and custom TFLite delegates to optimize design exploration and integration.
• The toolkit significantly reduces development time while achieving up to 4.2× speedup and improved energy efficiency on resource-constrained edge devices.

The SECDA-TFLite Design Toolkit is a methodology and suite of supporting tooling that streamlines the hardware/software co-design of FPGA-based Deep Neural Network (DNN) accelerators for edge inference within the TensorFlow Lite (TFLite) framework. Its principal aim is to reduce development time, enable efficient design space exploration, and facilitate the rapid integration of custom hardware accelerators with minimal software friction, targeting resource-constrained edge devices such as the Xilinx PYNQ-Z1. By leveraging SystemC-based simulation, automated host–accelerator interfacing, and integration with TFLite’s delegate architecture, the toolkit addresses challenges inherent in deploying DNNs on reconfigurable logic under tight computational and energy constraints.

1. Foundations and Motivation

Edge devices often rely on embedded processors or FPGAs to meet stringent power, memory, and latency requirements when running DNN inference workloads. Conventional CPU- or GPU-based inference is typically infeasible due to high energy or resource demands. FPGAs, with their reconfigurable logic, are well-suited to this purpose but introduce significant design overheads:

• High development times due to repeated synthesis passes and complexity in hardware–software interfacing.
• The necessity to tightly integrate with the high-level DNN frameworks, such as TFLite, that model inference as a sequence of computational kernels, often requiring the use of custom delegates to offload certain kernels.
• The challenge of mapping new or bottleneck DNN operators—e.g., convolutions, GEMMs, or transposed convolutions—into efficient hardware blocks while managing data transfer, scheduling, and memory bandwidth.

The SECDA-TFLite Design Toolkit is inspired by these challenges, seeking to merge rapid transaction-level simulation (via SystemC), modular driver generation, and integration with established toolchains to accelerate design iteration and validation (Haris et al., 2021).

2. Methodology and Workflow

The toolkit’s methodology—SystemC Enabled Co-design of DNN Accelerators (SECDA)—embeds several key process steps:

1. Simulation-First Design Exploration:
   • Accelerator kernels are first implemented and evaluated in a SystemC simulation environment, which enables rapid functional and performance validation at much higher speeds than hardware-in-the-loop or RTL-level simulation.
   • Early host-driver development occurs in tandem, with both sides simulated and verified together.
2. Hardware/Software Co-design:
   • The accelerator driver mediates between the TFLite runtime and hardware, managing buffer management, DMA data movement, and control flow.
   • SECDA-TFLite automates host code generation, abstracts the data marshaling required for TFLite’s tensor representations, and supports dynamic offloading through custom delegates.
3. Design Evaluation:
   • Design alternatives are primarily evaluated in simulation, using the following time model:

   $$E_t = (\#Sim \times (C_t + IS_t)) + (\#Synth \times (S_t + I_t))$$

   where $\#Sim$ is the number of simulation iterations (compile time $C_t$, inference simulation time $IS_t$), $\#Synth$ is the number of synthesis passes (synthesis time $S_t$, hardware inference time $I_t$). - Simulation typically runs over an order of magnitude (25×) faster than FPGA synthesis, substantially accelerating exploration.

4. Hardware Synthesis and System Integration:

   • Once an optimal design is determined through simulation, high-level synthesis (HLS) is used to generate hardware accelerators, which are integrated as custom delegates in the TFLite execution graph.
   • Final co-verification is performed, bridging simulated and hardware-driven execution.

3. Technical Features and Accelerator Integration

SECDA-TFLite is architected around the TFLite delegate API, enabling the selective offload of specific operator types to the custom accelerators:

• Delegate Mechanism: The toolkit generates delegates that identify and replace targeted operators (such as GEMM or TCONV) in a model’s execution plan. This enables hardware acceleration without modifications to the model graph.
• Accelerator Driver: The driver abstracts the memory layout translations between TFLite’s tensor memory and the accelerator, handling DMA transfers, tiling, and, where necessary, quantization and data format transformations.
• Simulation Environment: Transaction-level SystemC models allow fast, scalable simulation of both the accelerator datapath and its interfacing with the TFLite driver.

A practical case paper involves the MM2IM accelerator, which implements efficient transposed convolution using a matrix multiplication plus col2IM mapping, mathematically described by:

$$\text{out}(O_h, O_w, O_c) = \text{col2im}(\text{mm}(I, W_T), O_h, O_w, O_c)$$

where $I$ is the input tensor, $W_T$ is the reshaped filter, and $\text{col2im}$ transforms the flat result back into the target spatial output (Haris et al., 10 Jul 2025).

The SECDA-TFLite toolkit's infrastructure manages host–accelerator interfacing, synthetic benchmarking (covering >250 operator configurations), and exposes profiling hooks for identifying memory vs. compute bottlenecks.

4. Performance Evaluation and Outcomes

Experiments facilitated by SECDA-TFLite consistently report significant performance and efficiency gains:

• In the MM2IM case, the SECDA-TFLite synthesized accelerator on a PYNQ-Z1 FPGA achieved a mean 1.9× speedup over a highly optimized CPU baseline (ARM NEON, dual-thread) on synthetic TCONV benchmarks, and up to 4.2× on select configurations with high input channel counts (Haris et al., 10 Jul 2025).
• Full generative model inference (DCGAN, pix2pix) demonstrated up to 3× speedup and 2.4× energy reduction against the CPU.
• When compared across competing edge accelerators, the MM2IM design implemented with SECDA-TFLite achieved a throughput of 3.51 GOPs/DSP, at least 2 GOPs/DSP in excess of reported state-of-the-art designs.

The efficiency of the design process is also underscored by a drastic reduction in total design evaluation time, as measured by the time model above, due to the limited need for slow synthesis-based design validation passes (Haris et al., 2021).

5. Dataflow Optimization and Mapping Techniques

A key technical enabler within SECDA-TFLite is the ability to support sophisticated data movement and mapping optimizations:

• Input Handler and Scheduler: Custom modules optimize BRAM bandwidth utilization and sequence data fetches to minimize memory stalls and overlapping accesses.
• Mapper Modules: For operators such as TCONV, a Mapper module is included to accelerate permutation and accumulation tasks (e.g., resolving overlapping sums in input-oriented mapping), directly reducing the proportion of cycle time spent on output mapping (for MM2IM, lowering mapping cost from 35% of latency to a lower fraction).
• Quantization and Precision Control: The toolkit supports data format transformations, such as reducing output precision to 8 bits in output post-processing units to cut output transfer requirements by up to 4×.

Such optimizations collectively enable highly efficient pipeline utilization on the FPGA fabric and help manage limited memory resources.

6. Broader Applicability and Comparative Context

SECDA-TFLite embodies a general hardware/software co-design approach applicable to a range of DNN accelerator architectures and operator types:

• It is compatible with and extensible to various TFLite operator classes (e.g., convolution, depthwise, GEMM, TCONV), supporting integration across different DNN topologies.
• Unlike purely SDK-driven model optimization toolkits or those focused exclusively on quantization/model compression, SECDA-TFLite tightly couples accelerator hardware iteration with software driver and delegate design.
• As illustrated in comparative contexts (Bhat et al., 2020), SECDA-TFLite’s scope is distinguished from highly automated transfer-learning/model-creation tools; it addresses the needs of hardware-aware developers targeting low-level system optimization.

7. Open Source Availability and Resources

The core implementation, including SystemC models, accelerator designs, supporting drivers, and TFLite modifications, is available through the authors’ GitHub repository (https://github.com/gicLAB/SECDA). This repository serves as a reference for custom accelerator designers wishing to:

• Prototype and benchmark FPGA-based DNN accelerators in a full-stack simulation-to-hardware loop.
• Integrate new operator offloading strategies via custom TFLite delegates.
• Reproduce or extend published experiments and performance studies (Haris et al., 2021, Haris et al., 10 Jul 2025).

The availability of open-source resources is intended to accelerate the adoption and custom development of edge AI solutions leveraging the described methodologies.

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In summary, the SECDA-TFLite Design Toolkit facilitates the efficient co-design, rapid prototyping, and high-performance deployment of FPGA-based DNN accelerators under the TFLite framework. Its combination of simulation-driven iteration, modular driver generation, delegate-based model integration, and system-level data movement optimization addresses critical bottlenecks in resource-constrained edge inference deployments and supports scalable accelerator development for evolving deep learning workloads.