Hybrid Bit-Parallel and -Serial Processing for Flexible Precision AI Accelerator
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| Vydáno v: | ProQuest Dissertations and Theses (2025) |
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| On-line přístup: | Citation/Abstract Full Text - PDF |
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| 001 | 3228725986 | ||
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| 020 | |a 9798288801303 | ||
| 035 | |a 3228725986 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
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| 100 | 1 | |a Huang, Yuhua Benji | |
| 245 | 1 | |a Hybrid Bit-Parallel and -Serial Processing for Flexible Precision AI Accelerator | |
| 260 | |b ProQuest Dissertations & Theses |c 2025 | ||
| 513 | |a Dissertation/Thesis | ||
| 520 | 3 | |a Targeting the next generation of AI accelerators, FlexiBit has been proposed as a fully flexible-precision, bit-parallel architecture that efficiently supports both floating-point and integer arithmetic in arbitrary precisions and formats. By enabling true bit-parallel execution for any bitwidth—rather than relying on temporal bit-serial techniques—FlexiBit eliminates compute-unit underutilization and delivers substantial performance-and-area gains. Building on the FlexiBit foundation, this thesis presents a Hybrid Bit-parallel and Bit-serial Processing Architecture that delivers dynamic precision and performance scalability under tight area and energy constraints. This thesis design a dual-mode processing element that can operate in a wide, low-latency parallel mode or a narrow, energy-efficient serial mode, and rapidly switch between them at runtime. Across precisions from 1 to 64 bits, we systematically measure area, latency, and energy to characterize the full design space. Furthermore, we introduce a word-sliced scheme—partitioning an N-bit operand into K slices of P bits—to interpolate between pure parallel and pure serial extremes. Our results demonstrate that hybrid configurations can achieve near-parallel throughput with area and energy costs approaching those of purely serial designs, offering a practical, adaptable accelerator solution for AI workloads with varying accuracy and efficiency requirements. | |
| 653 | |a Computer engineering | ||
| 653 | |a Engineering | ||
| 653 | |a Electrical engineering | ||
| 653 | |a Artificial intelligence | ||
| 773 | 0 | |t ProQuest Dissertations and Theses |g (2025) | |
| 786 | 0 | |d ProQuest |t ProQuest Dissertations & Theses Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3228725986/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3228725986/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |