Low-power digital signal processing is required for edge devices operating in energy-constrained environments. Static multi-issue machines excel in such use cases but lack the required flexibility for maintaining high code density while exploiting instruction-level parallelism. This paper introduces a novel RISC-V-based DSP architecture, "Beaivi", that extends the processor with an exposed datapath multi-issue mode for exploiting instruction-level parallelism efficiently in performance-critical code regions while preserving high code density in noncritical phases with a RISC-V mode. The dynamic code density is further improved by leveraging a dictionary compression method that programs the dictionaries on a loop basis via compiler-driven static analysis. We demonstrate the real-world applicability of the architecture by taping out the processor using a commercial 22-nm technology. The design meets timing at 1.0 GHz and draws 50 mW under a neural network inference workload.

Offloading a computationally complex task from an edge device can improve latency and its battery life. The additional network transfers increase power consumption and latency, but can be mitigated with compression at the cost of additional computation and distortion. Thus, a balance between compression efficiency and complexity must be found and maintained as the network conditions change. In this paper, we propose an open -source edge offloading software stack that decides between local and remote task execution and chooses the optimal compression method based on continuously monitored system metrics under user-defined constraints. We evaluate the system offloading a semantic segmentation task from a smartphone over WiFi-6 and 5G networks, using latency, intersection over union (IoU), and power consumption metrics. Portability, multi-tenancy, and granular profiling are achieved by leveraging the PoCL-R OpenCL implementation. In simulated network impairments, dynamically selecting the compression strategy achieves 2.1-10.7% average latency improvement but maintains the highest possible quality when network conditions allow meeting the latency budget. If the computational overhead of compression surpasses the network transfer overhead, the system can transmit images uncompressed. Field measurements under network impairments confirm the usability of the system and its ability to fall back to local execution.

Remote inference allows lightweight edge devices, such as autonomous drones, to perform vision tasks exceeding their computational, energy, or processing delay budget. In such applications, reliable transmission of information is challenging due to high variations of channel quality. Traditional approaches involving spatio-temporal transforms, quantization, and entropy coding followed by digital transmission may be affected by a sudden decrease in quality (thedigital cliff) when the channel quality is less than expected during design. This problem can be addressed by using Linear Coding and Transmission (LCT), a joint source and channel coding scheme relying on linear operators only, allowing to achieve reconstructed per-pixel error commensurate with the wireless channel quality. In this paper, we propose CV-Cast: the first LCT scheme optimized for computer vision task accuracy instead of per-pixel distortion. Using this approach, for instance at 10 dB channel signal-to-noise ratio, CV-Cast requires transmitting 28% less symbols than a baseline LCT scheme in semantic segmentation and 15% in object detection tasks. Simulations involving a realistic 5G channel model confirm the smooth decrease in accuracy achieved with CV-Cast, while images encoded by JPEG or learned image coding (LIC) and transmitted using classical schemes at low Eb/N0 are subject to digital cliff.

In the above article [1], on page 1151, eq. (6), there is an error in the equation. The correct equation is: \begin{equation*} \min.\,\,D,\,\,\text{s.t.} \sum\limits_{k = 1}^K {{{\lambda }_k}\beta _k^2 \leqslant P.} \tag{6} \end{equation*} min.D,s.t.∑k=1Kλkβk2⩽P.(6)

Image communication increasingly involves machine-to-machine delivery. For example, images acquired by an autonomous drone can be compressed and sent to an edge server over a wireless network for resource-intensive processing. Traditional compression techniques involving transform, quantization, and entropy coding reach high compression efficiency, but channel conditions worse than expected may lead to a sharp decrease in the decoded image quality. As an alternative, Linear Coding and Transmission (LCT) systems have been proposed to avoid this digital cliff problem: The reconstructed image quality decreases gradually as channel conditions degrade. This paper presents a comprehensive evaluation of computer vision tasks with input images processed and transmitted using LCT. It also analyses the benefits of network retraining, accounting for impairments due to LCT and noisy channel. Considering object detection and semantic segmentation over images transmitted and received by LCT systems, we show that the task accuracy degrades smoothly when the channel quality decreases, avoiding the cliff effect. Retraining with noisy images processed by LCT restores detection mAP degradation from 23.8% to 4.4% and segmentation mIoU degradation from 43.2% to 8.1 % when the channel signal-to-noise ratio is 10 dB.

Latency-critical computer vision systems, such as autonomous driving or drone control, require fast image or video compression when offloading neural network inference to a remote computer. To ensure low latency on a near-sensor edge device, we propose the use of lightweight encoders with constant bitrate and pruned encoding configurations, namely, ASTC and JPEG XS. Pruning introduces significant distortion which we show can be recovered by retraining the neural network with compressed data after decompression. Such an approach does not modify the network architecture or require coding format modifications. By retraining with compressed datasets, we reduced the classification accuracy and segmentation mean intersection over union (mIoU) degradation due to ASTC compression to 4.9-5.0 percentage points (pp) and 4.4-4.0 pp, respectively. With the same method, the mIoU lost due to JPEG XS compression at the main profile was restored to 2.7-2.3 pp. In terms of encoding speed, our ASTC encoder implementation is 2.3x faster than JPEG. Even though the JPEG XS reference encoder requires optimizations to reach low latency, we showed that disabling significance flag coding saves 22–23% of encoding time at the cost of 0.4-0.3 mIoU after retraining.

This paper describes a low-power processor tailored for fast Fourier transform computations where transport triggering template is exploited. The processor is software-programmable while retaining an energy-efficiency comparable to existing fixed-function implementations. The power savings are achieved by compressing the computation kernel into one instruction word. The word is stored in an instruction loop buffer, which is more power-efficient than regular instruction memory storage. The processor supports all power-of-two FFT sizes from 64 to 16384 and given 1 mJ of energy, it can compute 20916 transforms of size 1024.