Quantised ConvLSTM Accelerator

• Quantised ConvLSTM Accelerator is a system that uses reduced-precision arithmetic and FPGA/ASIC techniques to efficiently execute ConvLSTM neural networks.
• It leverages advanced quantisation schemes such as binary, ternary, and mixed-precision to balance accuracy, throughput, and energy consumption.
• The design integrates dynamic precision control and dataflow compilation to optimize resource utilization and enable real-time, edge computing applications.

A Quantised ConvLSTM Accelerator is a hardware or software system for efficiently executing Convolutional Long Short-Term Memory (ConvLSTM) neural networks using reduced-precision numerical formats. The accelerator leverages aggressive quantisation, specialized microarchitectural techniques, and often field-programmable gate arrays (FPGAs) or custom processors to balance throughput, energy consumption, inference accuracy, and resource usage. Modern designs employ mixed-precision arithmetic, selective quantisation strategies for weights and activations, and dataflow-oriented compilation to realize low-latency, power-efficient inference suitable for edge and real-time applications.

1. Quantisation Schemes for ConvLSTM

Quantisation is at the core of any ConvLSTM accelerator scheme. Common quantisation methods include:

• Binary Connect (BC): Weights are constrained to $\{-1, 1\}$, with the quantisation rule $\text{quantized weight} = 1$ if $w \geq 0$, else $-1$ (Eq. 20). This approach replaces multiplications in the forward pass with additions and subtractions, significantly reducing arithmetic complexity and memory requirements (1802.02615).
• Ternary Connect (TC): Weights are quantised to $\{-1, 0, 1\}$ based on the mean ($\mu$) and standard deviation ($\sigma$) of the layer’s weights:

$$w = \begin{cases} -1 & \text{if } w \leq -(\mu+\sigma) \ 0 & \text{if } -(\mu+\sigma) < w \leq (\mu+\sigma) \ 1 & \text{if } w > (\mu+\sigma) \end{cases}$$

(Eq. 21), with alternatives for uniform distribution (Eq. 22).

• Quaternary Connect (QC): Quantises to $\{-1, -0.5, 0.5, 1\}$ using adaptive thresholds:

$$w = \begin{cases} -1 & \text{if } w \leq -(\mu+2\sigma) \ -0.5 & \text{if } -(\mu+2\sigma) < w \leq 0 \ 0.5 & \text{if } 0 < w \leq (\mu+2\sigma) \ 1 & \text{if } w > (\mu+2\sigma) \end{cases}$$

(Eq. 23), with a uniform variant (Eq. 24) (1802.02615). These techniques are derived adaptively using weight statistics, maintaining statistical properties of the original parameters.

Recent FPGA-oriented flows introduce "mixed-precision" quantisation, where each quantiser—internal to the ConvLSTM gates and nonlinearities—may have a distinct bit-width, such as W8A6 (8 bits for weights, 6 bits for activations) (2506.20810). The quantisation is expressed explicitly in intermediate representations such as ONNX graphs using QCDQ (QuantizeLinear–Clip–DequantizeLinear) operator chains, which are eventually merged into quantisers suitable for hardware logic.

Ultra-low-precision strategies, such as packing 1- to 4-bit operands into registers (ULPPACK), are supported in emerging vector processors (2306.09905). These allow dot-product and convolution computations for ConvLSTM gates at sub-byte precision, substantially boosting arithmetic density.

2. Accelerator Architectures and Compilation Methodologies

Quantised ConvLSTM accelerators typically target FPGAs or ASICs, composing the following elements:

• Multiply-Accumulate Units (MACs): Fixed-point or sub-byte MACs are instantiated as pipelines. For instance, "Cell Body Units" realize convolution via Q-format fixed-point arithmetic (2109.03040). Pipelined adder trees and register-level caches support high throughput for varied kernel dimensions.
• Dynamic Precision Units: Recent architectures support dynamic selection of numerical precision for each operation (e.g., 4-bit or 8-bit), depending on the stability of the ConvLSTM cell state. This is managed by hardware modules such as a Peak Detector Unit (PDU), which tracks recent cell state ranges

$$r = \max C_i - \min C_i, \quad \text{switch between precisions using } r \pm r\cdot\beta$$

to maintain accuracy and efficiency (1911.04244).

• Custom Vector Processors: Systems like Sparq modify RISC-V vector cores to support sub-byte multiply-shift-accumulate instructions (e.g., vmacsr), permitting highly parallel processing of quantised tensors and removing floating-point arithmetic units to minimize area and power (2306.09905).
• Compilation Flows: Advanced toolchains (e.g., FINN) map quantised ONNX graphs to HLS (High Level Synthesis) kernels using operator-inlining and transformation passes. The recurrent Scan operator in ONNX is leveraged to expose the full structure of ConvLSTM computations for correct quantiser placement and efficient hardware instantiation (2506.20810).

3. Quantisation-Aware Optimisations and Trade-offs

To maximize both accuracy and resource savings, several quantisation-aware optimisations are employed:

• Mixed-precision tuning: Fine-grained assignment of bit-widths for each gate, activation, or convolution. Higher precision is assigned to critical paths, while less sensitive components are further quantised, balancing energy and accuracy (2506.20810).
• Operator Transformations: Floating-point operations associated with quantisers are collapsed to integer threshold-compare operations:

$$\text{QuantizeLinear} + \text{Clip} + \text{DequantizeLinear} \rightarrow \text{Quant}$$

Nonlinearities such as tanh or sigmoid are replaced by multi-threshold comparators or integer look-up tables (2506.20810).

• Packing and Parallelism: Packing multiple sub-byte values (e.g., 4×2-bit or 2×4-bit) into a register and operating directly on them via vector instructions (e.g., Sparq vmacsr) increases the effective operations-per-cycle while minimizing memory bandwidth (2306.09905).
• Quantisation-Aware Training: The ConvLSTM is trained or fine-tuned with simulated quantisation in the loop, producing weights that are robust to precision loss (1802.02615).
• Low-rank and Pruning: For ConvLSTM with large filter banks, low-rank filter decompositions or pruning inactive weights/clusters are applied to further reduce multiply counts and memory traffic (1905.03577).

4. Empirical Performance and Resource Evaluation

Multiple experimental studies demonstrate the efficacy and efficiency of quantised ConvLSTM accelerators:

• Accuracy: Testing on tasks such as sentiment analysis (IMDB) and video frame prediction (moving MNIST) found less than 2–3% accuracy degradation for ternary and quaternary quantised models compared to full-precision, with binary quantisation causing more noticeable loss (1802.02615). Mixed-precision (e.g., W8A6) models can match or outperform state-of-the-art baselines in mid-price stock prediction (2506.20810).
• Energy and Throughput: Energy consumption and runtime are significantly reduced. For LSTM accelerators, dynamic precision selection yields up to $1.56\times$ speedup and $23\%$ energy savings without accuracy loss (1911.04244). Sub-byte vectorized convolution achieves $3.2\times$ (2-bit) and $1.7\times$ (4-bit) acceleration over optimized 16-bit convolution (2306.09905).
• FPGA Implementation: Tested designs implement ConvLSTM quantised models on FPGAs (e.g., Xilinx Virtex-7, XCZU7EV), achieving latencies on the order of 4.3 ms for sequence batch size 1 and using 49% of LUTs and 15% of DSPs for medium-sized models (2506.20810). Maximum throughput reached 226.2 GOPS for $3\times3$ convolution kernels on the Virtex-7 across a range of kernel sizes (2109.03040).
• Resource Utilization: The reduction in precision from 32-bit floating point to fixed-point or sub-byte allows significant DSP, LUT, and memory savings. Removing the floating point unit in custom vector processors reduces area by approximately 43.3% and power by 58.8% (2306.09905).

5. Applications and Domains

Quantised ConvLSTM accelerators have been deployed or prototyped for a diverse range of real-time and resource-constrained applications:

• Time-Series Forecasting: High-frequency stock prediction, where inference intervals must be less than 192 ms, is enabled by quantised ConvLSTM accelerators running under 4.3 ms per inference (2506.20810).
• Hyperspectral Image Classification: Architectures using ConvLSTM2D and ConvLSTM3D preserve spatial and spectral structure for superior classification of hyperspectral data, with state-of-the-art accuracy when training samples are limited (1905.03577).
• Video Frame Prediction: ConvLSTM quantised models produce competitive frame reconstructions for moving MNIST, indicating their viability for embedded vision (1802.02615).
• General Edge AI: The small area, low power, and integer-only arithmetic make these accelerators suitable for edge computing, robotics, and real-time signal processing (2306.09905, 2109.03040).

6. Challenges, Limitations, and Future Directions

Several challenges are inherent to the design of quantised ConvLSTM accelerators:

• Accuracy Degradation: Binary and aggressive quantisation schemes can result in notable accuracy loss. Ternary and quaternary approaches offer a better balance, particularly with adaptive, statistics-driven thresholds (1802.02615).
• Threshold Sensitivity: The adaptive quantisation method requires careful tuning of thresholds; small changes may lead to sparsity or effectively binary weight distributions (1802.02615).
• Sequential Dependencies: The inherent recurrence in ConvLSTM limits intra-layer parallelism compared to standard CNNs. Emerging approaches duplicate memory cells or use pipeline unrolling to improve throughput at the cost of area (2506.20810).
• Spatial Granularity in Precision Control: Applying dynamic precision (e.g., per-feature map, per-pixel, or per-channel) introduces trade-offs between area overhead, control logic complexity, and the effectiveness of precision switching (1911.04244).
• Hardware Mapping: Packing mixed-precision operations and managing memory layout, bandwidth, and quantisation indices (especially for spatially large models) remains non-trivial (2306.09905, 2109.03040).

Anticipated future advancements include refining quantiser placement, enhancing on-chip scheduling (to exploit data reuse and reduce latency), integrating more aggressive low-rank and pruning techniques, and further generalising compilation flows to recurrent architectures beyond ConvLSTM (2506.20810).

7. Broader Significance and Research Trajectory

The quantised ConvLSTM accelerator field exemplifies the convergence of algorithmic innovation (quantisation-aware training, adaptive thresholds) and specialized hardware design (sub-byte computation, dynamic precision scheduling, dataflow-driven HLS compilation). The integration of standardized intermediate representations (such as ONNX with explicit quantisers and recurrence via Scan) allows generalisable hardware flows supporting evolving neural architectures (2506.20810). As advances in quantisation minimize the precision gap and enable reliable integer-only circuits, quantised ConvLSTM accelerators are increasingly deployed in applications that require tight power, resource, or latency constraints, marking a key direction for the future of embedded deep sequential modeling.