This work presents a hardware–software co-design whose 16-bit tag uses odd parity and fail-safe class encoding, which combines a variable-precision extent, an aligned CRC checker, and an exact-bounds micro-cache.
Abstract
Graphics processing units (GPUs) underpin high-performance computing, but device partitioning does not ensure that an in-context pointer remains within its allocation. We present a hardware–software co-design whose 16-bit tag uses odd parity and fail-safe class encoding. It combines a variable-precision extent, an aligned CRC checker, and an exact-bounds micro-cache. For 200,000 log-uniform requests from 16 B to 1 TiB, mean Class 4 fragmentation is 1.638%, versus 27.733% for power-of-two encoding. The seven-bit aligned signature has full GF(2) rank and a 1/128 non-adaptive collision rate; its deterministic window is exactly one through three regions and tight at four. Exhaustive testing rejects every one-bit tag corruption, while two-bit analysis demonstrates why parity is not adversarial authentication. A SAT-equivalent endpoint rewrite reduces Class 3 generic depth from 60 to 24 levels. A fail-closed 32-lane Class 4 topology detects nonuniform active-lane tags in hardware; for uniform tags, it reduces generic CMOS cost from 155,200 to 72,872 transistor equivalents (53.05%) and depth from 67 to 61 levels. Official SASS traces provide an analytical exposure bound rather than native simulation; a separate pre-layout 45 nm mapping is reported only as a timing sensitivity experiment.
This research presents an optimized, reduced-clock-cycle approach for performing 128-bit floating-point calculations on Field-Programmable Gate Arrays (FPGAs) using the SRMA architecture, which not only reduces execution time but also decreases energy consumption on Kintex-7 FPGA boards.
N. P, Thirumalaiswamy V., H. S. et al.· International Journal of Com...· 0 citations
FlexXNOR-OD is proposed, a new FPGA-oriented accelerator for real-time object detection that combines channel-group precision assignment with a lane-fusible XNOR processing element that offers a practical path toward precision-scalable, multiplier-light object detection on resource-constrained edge platforms.
Budidha Sriman and D. Sateesh· International Journal of Adv...· 0 citations
It is shown that CGLA-offloaded BitNet C++ execution measures 2.52 tokens/s, and the mapping adds OP_SMA4 as a reusable signed-int4 multiply-accumulate instruction rather than a BitNet-only datapath.
Across three decoder architectures on an H100 the authors measure that parse, not copy, holds 64-72% of device-resident decode time; that bounding back-reference chain depth - provable, and costing 0.006% in ratio - moves latency by at most 2.8% and, for the file's own latency spike, provably by nothing at all.
Modern GPU kernels increasingly stress the instruction supply path, while fixed instruction containers can leave substantial footprint slack. This paper presents an automated encoding-synthesis framework that treats instruction layout as a constrained slot-assignment problem over a validated instruction-form field spec...
Cryptographic hash functions over integers modulo a prime play a decisive role in the efficiency and security of proof systems for computational integrity. Early designs focused on compact arithmetic circuits and efficient software execution, primarily targeting general-purpose CPUs rather than hardware accelerators. T...
Luca Campa, Thomas De Cnudde, A. Kindi et al.· 0 citations
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 6, 2026
Writing as a participant and researcher, PhD student JS Tan SM ’22 has co-authored a new book about the rise of tech worker protests and the employer backlash that followed.
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