arXiv subjects

Christodoulos Peltekis

Publications and source records attributed to Christodoulos Peltekis.

7 recordsLinked to original sources

arXiv2025

Periodic Online Testing for Sparse Systolic Tensor Arrays

Modern Machine Learning (ML) applications often benefit from structured sparsity, a technique that efficiently reduces model complexity and simplifies handling of sparse data in hardware. Sparse systolic tensor arrays - specifically designed to accelerate these structured-sparse ML models - play a pivotal role in enabling efficient computations. As ML is increasingly integrated into safety-critical systems, it is of paramount importance to ensure the reliability of these systems. This paper introduces an online error-checking technique capable of detecting and locating permanent faults within sparse systolic tensor arrays before computation begins. The new technique relies on merely four test vectors and exploits the weight values already loaded within the systolic array to comprehensively test the system. Fault-injection campaigns within the gate-level netlist, while executing three well-established Convolutional Neural Networks (CNN), validate the efficiency of the proposed approach, which is shown to achieve very high fault coverage, while incurring minimal performance and area overheads.

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arXiv2025

Optimizing Structured-Sparse Matrix Multiplication in RISC-V Vector Processors

Structured sparsity has been proposed as an efficient way to prune the complexity of Machine Learning (ML) applications and to simplify the handling of sparse data in hardware. Accelerating ML models, whether for training, or inference, heavily relies on matrix multiplications that can be efficiently executed on vector processors, or custom matrix engines. This work aims to integrate the simplicity of structured sparsity into vector execution to speed up the corresponding matrix multiplications. Initially, the implementation of structured-sparse matrix multiplication using the current RISC-V instruction set vector extension is comprehensively explored. Critical parameters that affect performance, such as the impact of data distribution across the scalar and vector register files, data locality, and the effectiveness of loop unrolling are analyzed both qualitatively and quantitatively. Furthermore, it is demonstrated that the addition of a single new instruction would reap even higher performance. The newly proposed instruction is called vindexmac, i.e., vector index-multiply-accumulate. It allows for indirect reads from the vector register file and it reduces the number of instructions executed per matrix multiplication iteration, without introducing additional dependencies that would limit loop unrolling. The proposed new instruction was integrated in a decoupled RISC-V vector processor with negligible hardware cost. Experimental results demonstrate the runtime efficiency and the scalability offered by the introduced optimizations and the new instruction for the execution of state-of-the-art Convolutional Neural Networks. More particularly, the addition of a custom instruction improves runtime by 25% and 33% when compared with highly-optimized vectorized kernels that use only the currently defined RISC-V instructions.

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arXiv2024

GCN-ABFT: Low-Cost Online Error Checking for Graph Convolutional Networks

Graph convolutional networks (GCNs) are popular for building machine-learning application for graph-structured data. This widespread adoption led to the development of specialized GCN hardware accelerators. In this work, we address a key architectural challenge for GCN accelerators: how to detect errors in GCN computations arising from random hardware faults with the least computation cost. Each GCN layer performs a graph convolution, mathematically equivalent to multiplying three matrices, computed through two separate matrix multiplications. Existing Algorithm-based Fault Tolerance(ABFT) techniques can check the results of individual matrix multiplications. However, for a GCN layer, this check should be performed twice. To avoid this overhead, this work introduces GCN-ABFT that directly calculates a checksum for the entire three-matrix product within a single GCN layer, providing a cost-effective approach for error detection in GCN accelerators. Experimental results demonstrate that GCN-ABFT reduces the number of operations needed for checksum computation by over 21% on average for representative GCN applications. These savings are achieved without sacrificing fault-detection accuracy, as evidenced by the presented fault-injection analysis.

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arXiv2024

Floating-Point Multiply-Add with Approximate Normalization for Low-Cost Matrix Engines

The widespread adoption of machine learning algorithms necessitates hardware acceleration to ensure efficient performance. This acceleration relies on custom matrix engines that operate on full or reduced-precision floating-point arithmetic. However, conventional floating-point implementations can be power hungry. This paper proposes a method to improve the energy efficiency of the matrix engines used in machine learning algorithm acceleration. Our approach leverages approximate normalization within the floating-point multiply-add units as a means to reduce their hardware complexity, without sacrificing overall machine-learning model accuracy. Hardware synthesis results show that this technique reduces area and power consumption roughly by 16% and 13% on average for Bfloat16 format. Also, the error introduced in transformer model accuracy is 1% on average, for the most efficient configuration of the proposed approach.

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arXiv2024

Reusing Softmax Hardware Unit for GELU Computation in Transformers

Transformers have improved drastically the performance of natural language processing (NLP) and computer vision applications. The computation of transformers involves matrix multiplications and non-linear activation functions such as softmax and GELU (Gaussion Error Linear Unit) that are accelerated directly in hardware. Currently, function evaluation is done separately for each function and rarely allows for hardware reuse. To mitigate this problem, in this work, we map the computation of GELU to a softmax operator. In this way, the efficient hardware units designed already for softmax can be reused for computing GELU as well. Computation of GELU can enjoy the inherent vectorized nature of softmax and produce in parallel multiple GELU outcomes. Experimental results show that computing GELU via a pre-existing and incrementally modified softmax hardware unit (a) does not reduce the accuracy of representative NLP applications and (b) allows the reduction of the overall hardware area and power by 6.1% and 11.9%, respectively, on average.

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arXiv2024

Error Checking for Sparse Systolic Tensor Arrays

Structured sparsity is an efficient way to prune the complexity of modern Machine Learning (ML) applications and to simplify the handling of sparse data in hardware. In such cases, the acceleration of structured-sparse ML models is handled by sparse systolic tensor arrays. The increasing prevalence of ML in safety-critical systems requires enhancing the sparse tensor arrays with online error detection for managing random hardware failures. Algorithm-based fault tolerance has been proposed as a low-cost mechanism to check online the result of computations against random hardware failures. In this work, we address a key architectural challenge with structured-sparse tensor arrays: how to provide online error checking for a range of structured sparsity levels while maintaining high utilization of the hardware. Experimental results highlight the minimum hardware overhead incurred by the proposed checking logic and its error detection properties after injecting random hardware faults on sparse tensor arrays that execute layers of ResNet50 CNN.

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arXiv2024

DeMM: A Decoupled Matrix Multiplication Engine Supporting Relaxed Structured Sparsity

Deep Learning (DL) has achieved unprecedented success in various application domains. Meanwhile, model pruning has emerged as a viable solution to reduce the footprint of DL models in mobile applications, without compromising their accuracy. To enable the matrix engines built for dense DL models to also handle their pruned counterparts, pruned DL models follow a fine-grained structured sparsity pattern of 1:4, or 2:4, whereby in each group of four contiguous values, at least one, or two, respectively, must be non-zero. Structured sparsity has recently also moved to coarser (relaxed) cases of N:128, or N:256, for small values of N, targeting a wider range of sparsity (10%-90%) for the DL models. In this work, we design an accelerator that operates, by construction, on wide blocks with relaxed structured sparsity. In contrast to the conventional systolic array archetype, the new engine decouples the memory part of the systolic array from the multiply-add units. The memory block comprises 1 write and N read ports, with the number of read ports being equal to the number of non-zero elements per row. The multiply-add units connect directly to each read port and complete the multiplication in a row-wise product-first order. More importantly, simple reconfiguration facilitates more dense patterns. The experimental evaluation demonstrates substantial latency improvements over current state-of-the-art systolic array engines built for fine-grained and relaxed structured sparsity.

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