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This is an undeniable fact now that wireless systems pervade all aspects of our lives. These systems are evolving at a rapid clip, connecting more people and devices every single day that goes by. This growth is further fueled by the users’ insatiable appetite for more traffic, be it for online gaming, watching high-fidelity video, downloading huge files, live- streaming and many more uses. With the advent of internet of things (IoT), which brings a countless number devices and sensors into the picture, this growth turns into an unstoppable force. Catering to the connectivity and data rate demands that these applications and devices place on the wireless communi- cations infrastructure is not a trivial issue. As the old 4G systems are approaching, or rather have already surpassed, their limits, the new kids on the block are 5G and what comes beyond. These systems are developed specifically to bump up the data rates, provide better coverage, and increase the overall energy and spectral efficiencies. In order to facilitate this, a number of key technologies have proven themselves instrumental. One such technology is the massive multiple-input multiple-output (MIMO), which scales up the number of antennas available in the base station (BS) to the hundreds, in order to add space as yet another degree of freedom to the system, creating the holy trinity of time-frequency-space. This is crucial, considering the fact that frequency resources are limited, very expensive, and already overcrowded. This idea can be, and is being, pushed even further by employing thousands of antennas in systems such as large intelligent surfaces (LISs).But it is not all moonlight and roses, as one might think. Incorporating these many antennas in the system puts a huge burden on data processing and data marshaling subsystems. A centralized approach does not carry the day here, and distributing the processing is not a piece of cake either. That is what this thesis concerns itself with, i.e., how

Communication channels with memory are often sensitive to burst noise, which drastically reduces the decoding performance of standard channel code decoders, and this degradation worsens as channel memory increases. Hence, interleavers and de-interleavers are usually used to reduce the effects of burst noise at the expense of increased latency in the communication system. The delay imposed by interleavers/de-interleavers and the performance deterioration induced by channel memory are unacceptable in novel applications that require ultra-low latency and high decoding performance. Guessing Random Additive Noise Decoding (GRAND) is a universal Maximum Likelihood (ML) decoding technique for short-length and high-rate channel codes. GRAND Markov Order (GRAND-MO) is a hard-input variant of GRAND that has been specifically developed for communication channels with memory that are subject to burst noise. GRAND-MO can be used directly on hard demodulated channel signals, removing the requirement for extra interleavers/de-interleavers and considerably reducing overall latency in communication systems. This paper describes a high-throughput GRAND-MO VLSI architecture that can achieve an average throughput of up to 52 Gbps and 64 Gbps for code lengths of 128 and 79, respectively. Furthermore, we propose improvements to the GRAND-MO algorithm to simplify hardware implementation and reduce decoding complexity. When compared to GRANDAB, a hard-input variant of GRAND, the proposed improved GRAND-MO algorithm yields a decoding performance gain of 2 ∼ 3 dB at a target FER of 10 - 5. Similarly, as compared to the (79, 64) BCH code decoder, the proposed GRAND-MO decoder has a 33% reduced worst-case latency and a 2 dB gain in decoding performance at a target FER of 10 - 5. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Ultrareliable low-latency communication (URLLC), a major 5G new-radio (NR) use case, is the key enabler for applications with strict reliability and latency requirements. These applications necessitate the use of short-length and high-rate channel codes. Guessing random additive noise decoding (GRAND) is a recently proposed maximum likelihood (ML) decoding technique for these short-length and high-rate codes. Rather than decoding the received vector, GRAND tries to infer the noise that corrupted the transmitted codeword during transmission through the communication channel. As a result, GRAND can decode any code, structured or unstructured. GRAND has hard-input as well as soft-input variants. Among these variants, ordered reliability bits GRAND (ORBGRAND) is a soft-input variant that outperforms hard-input GRAND and is suitable for parallel hardware implementation. This work reports the first hardware architecture for ORBGRAND, which achieves an average throughput of up to 42.5 Gb/s for a code length of 128 at a target frame error rate (FER) of 10-7. Furthermore, the proposed hardware can be used to decode any code as long as the length and rate constraints are met. In comparison to the GRAND with ABandonment (GRANDAB), a hard-input variant of GRAND, the proposed architecture enhances decoding performance by at least 2 dB. When compared to the state-of-the-art fast dynamic successive cancellation flip decoder (Fast-DSCF) using a 5G polar code (PC) (128, 105), the proposed ORBGRAND VLSI implementation has 49times higher average throughput, 32times times more energy efficiency, and 5times more area efficiency while maintaining similar decoding performance. © 1993-2012 IEEE.

Guessing Random Additive Noise Decoding (GRAND) is a recently proposed Maximum Likelihood (ML) decoding technique. Irrespective of the structure of the error correcting code, GRAND tries to guess the noise that corrupted the codeword in order to decode any linear error-correcting block code. GRAND Markov Order (GRAND-MO) is a variant of GRAND that is useful to decode error correcting code transmitted over communication channels with memory which are vulnerable to burst noise. Usually, interleavers and de-interleavers are used in communication systems to mitigate the effects of channel memory. Interleaving and de-interleaving introduce undesirable latency, which increases with channel memory. To prevent this added latency penalty, GRAND-MO can be directly used on the hard demodulated channel signals. This work reports the first GRAND-MO hardware architecture which achieves an average throughput of up to 52 Gbps and 64 Gbps for a code length of 128 and 79 respectively. Compared to the GRANDAB, hard-input variant of GRAND, the proposed architecture achieves 3 dB gain in decoding performance for a target FER of 10-5. Similarly, comparing the GRAND-MO decoder with a decoder tailored for a (79, 64) BCH code showed that the proposed architecture achieves 33% higher worst case throughput and 2 dB gain in decoding performance. © 2021 IEEE.

In this brief, a novel approximation method and its optimized hardware implementation are proposed for the sigmoid function used in Deep Neural Networks (DNNs). Based on piecewise approximation and truncated Taylor series expansion, the proposed method achieves very good approximation with low complexity while exploiting data representation with powers of two. In addition, by analyzing gradients of the sigmoid function, a small trick is introduced to improve the approximation precision. Furthermore, to reduce the hardware complexity and shorten the critical path, sampled values of the function are generated with simple logical-mapping. It is shown that the proposed approximation schemes can be implemented with purely combinational logic and the sigmoid function can be computed in one clock cycle. The experimental results demonstrate that the mean absolute errors are at the order of 1 x 10(-3). Compared with prior arts, the new design can obtain significant improvement in critical path with comparable performance., QC 20210112

Stochastic computing (SC) has been applied on the implementations of complex arithmetic functions. Complicated polynomial-based approximations lead to large hardware complexity of previous SC circuits for arithmetic functions. In this paper, a novel piecewise approximation method based on Taylor series expansion is proposed for complex arithmetic functions. Efficient implementations based on unipolar stochastic logic are presented for the monotonic functions. Furthermore, detailed optimization schemes are provided for the non-monotonic functions. Using NAND and AND gates as main computing elements, the optimized hardware architectures have extremely low complexity. The experimental results show that a broad range of arithmetic functions can be implemented with the proposed SC circuits, and the mean absolute errors can achieve the order of 1 x 10(-3). Compared with the state-of-the-art works, the approximation precision for some typical functions can be increased by more than 8x with our method. In addition, the proposed circuits outperform the previous methods in hardware complexity and critical path significantly., QC 20200504

Guessing Random Additive Noise Decoding (GRAND) is a recently proposed universal decoding algorithm for linear error correcting codes. Since GRAND does not depend on the structure of the code, it can be used for any code encountered in contemporary communication standards or may even be used for random linear network coding. This property makes this new algorithm particularly appealing. Instead of trying to decode the received vector, GRAND attempts to identify the noise that corrupted the codeword. To that end, GRAND relies on the generation of test error patterns that are successively applied to the received vector. In this paper, we propose the first hardware architecture for the GRAND algorithm. Considering GRAND with ABandonment (GRANDAB) that limits the number of test patterns, the proposed architecture only needs 2 + Sigma(n)(i=2)[i/2] time steps to perform the Sigma(3)(i=1) ((n) (i)) queries required when AB = 3. For a code length of 128, our proposed hardware architecture demonstrates only a fraction (1.2%) of the total number of performed queries as time steps. Synthesis result using TSMC 65nm CMOS technology shows that average throughputs of 32 Gbps to 64 Gbps can be achieved at an SNR of 10 dB for a code length of 128 and code rates rate higher than 0.75, transmitted over an AWGN channel. Comparisons with a decoder tailored for a (79, 64) BCH code show that the proposed architecture can achieve a slightly higher average throughput at high SNRs, while obtaining the same decoding performance.

doctoral thesis

Digital electronic industry today relies on Nyquist sampling theorem, which requires to double the size (sampling rate) of the signal representation on the Fourier basis to avoid information loss. However, most natural signals have very sparse representations on some other orthogonal (non-Fourier) basis. This mismatch implies a large redundancy in Nyquist-sampled data, making compression a necessity prior to storage or transmission. Recent advances in compressive sensing (CS) theory offer us an alternative data acquisition framework, which can greatly impact power-starved applications such as wireless sensors. CS techniques provide a universal approach to sample compressible signals at a rate significantly below the Nyquist rate with limited information loss. Therefore, CS is a promising technology for realizing configurable, cost-effective, miniaturized, and ultra-low-power data acquisition devices for mobile and wearable applications.However, the digital signal processing of compressively-sampled data involves solving a sparse approximation problem, which requires iterative-searching algorithms that have high computational complexity and require intensive memory access. As a result, existing software solutions are neither energy-efficient nor cost-effective for real-time processing of compressively-sampled data, especially when the processing is to be performed on energy-limited devices. To solve this problem, this dissertation presents a scalable VLSI architecture that can be implemented on field-programmable gate arrays (FPGAs) or system-on-chips (SoCs) to perform dedicated-hardware-driven sparse approximation. A VLSI soft-IP core of the sparse approximation engine is developed in Verilog-HDL, which supports a floating-point data format with 10 design parameters, providing a high dynamic range and the flexibility for application-specific user customizations. Taking advantage of the algorithm-architecture co-design that leverages algorithm reformulations, configu

Digital electronic industry today relies on Nyquist sampling theorem, which requires to double the size (sampling rate) of the signal representation on the Fourier basis to avoid information loss. However, most natural signals have very sparse representations on some other orthogonal (non-Fourier) basis. This mismatch implies a large redundancy in Nyquist-sampled data, making compression a necessity prior to storage or transmission. Recent advances in compressive sensing (CS) theory offer us an alternative data acquisition framework, which can greatly impact power-starved applications such as wireless sensors. CS techniques provide a universal approach to sample compressible signals at a rate significantly below the Nyquist rate with limited information loss. Therefore, CS is a promising technology for realizing configurable, cost-effective, miniaturized, and ultra-low-power data acquisition devices for mobile and wearable applications.However, the digital signal processing of compressively-sampled data involves solving a sparse approximation problem, which requires iterative-searching algorithms that have high computational complexity and require intensive memory access. As a result, existing software solutions are neither energy-efficient nor cost-effective for real-time processing of compressively-sampled data, especially when the processing is to be performed on energy-limited devices. To solve this problem, this dissertation presents a scalable VLSI architecture that can be implemented on field-programmable gate arrays (FPGAs) or system-on-chips (SoCs) to perform dedicated-hardware-driven sparse approximation. A VLSI soft-IP core of the sparse approximation engine is developed in Verilog-HDL, which supports a floating-point data format with 10 design parameters, providing a high dynamic range and the flexibility for application-specific user customizations. Taking advantage of the algorithm-architecture co-design that leverages algorithm reformulations, configu

This paper presents an asynchronous VLSI architecture for modeling the oscillatory patterns seen in segmented biological systems. The architecture emulates the intersegmental synaptic connectivity observed in these biological systems. The communications network uses address-event representation (AER), a common neuromorphic protocol for data transmission. The asynchronous circuits are synthesized using communicating hardware processes (CHP) procedures. The architecture is scalable, supports multichip communication, and operates independent of the type of silicon neuron (spiking or burst envelopes). A 16-segment prototype system was developed, tested, and implemented, data from this system are presented. © 2006 IEEE.

The third generation (3G) of cellular communications standards is based on wideband CDMA. The wideband signal experiences frequency selective fading due to multipath propagation. To mitigate this effect, a RAKE receiver is typically used to coherently combine the signal energy received on different multipaths. An effective multipath searcher is, therefore, required to identify the delayed versions of the transmitted signal with low probability of false alarm and misdetection. This paper presents an efficient and novel WCDMA multipath searcher design and VLSI architecture that provides a good compromise between complexity, performance, and power consumption. Novel multipath searcher algorithms such as time domain interleaving and peak detection are also presented. The proposed searcher was implemented in 0.18 mu m CMOS technology and requires only 150 k gates for a total area of 1.5 mm(2) consuming 6.6 mw at 100 MHz. The functionality and performance of the searcher was verified under realistic conditions using a channel emulator.

The third generation (3G) of cellular communications standards is based on wideband CDMA. The wideband signal experiences frequency selective fading due to multipath propagation. To mitigate this effect, a RAKE receiver is typically used to coherently combine the signal energy received on different multipaths. An effective multipath searcher is, therefore, required to identify the delayed versions of the transmitted signal with low probability of false alarm and misdetection. This paper presents an efficient and novel WCDMA multipath searcher design and VLSI architecture that provides a good compromise between complexity, performance, and power consumption. Novel multipath searcher algorithms such as time domain interleaving and peak detection are also presented. The proposed searcher was implemented in 0.18 mu m CMOS technology and requires only 150 k gates for a total area of 1.5 mm(2) consuming 6.6 mw at 100 MHz. The functionality and performance of the searcher was verified under realistic conditions using a channel emulator.

With VLSI architecture the chip area is a better measure of cost than the conventional gate count. We show that addition of n-bit binary numbers can be performed on a chip in time proportional to log n and with area proportional to n log n. (Author), Prepared in cooperation with Australian National Univ., Canberra. Dept. of Computer Science.