Searcharxiv⌕ Search

arXiv subjects

Daniel Keller

Publications and source records attributed to Daniel Keller.

3 recordsLinked to original sources

Stop Paying by the Letter: Accelerating Full-Chip RTL Simulation with Host-Side Tokenized Logging

Firmware diagnostic logging (printf) is an essential debug instrument in full-chip SoC design verification, yet it imposes a heavy tax on cycle-accurate RTL simulation: formatting a 30-character, 1-argument message on a simulated CPU and serializing it through an MMIO aperture costs 3.04 to 15.23 $μ$s per call (15.27 $μ$s unweighted / 15.08 $μ$s print-weighted critical-path mean on Suite A). Unlike embedded tokenizers that target flash footprint and wire bytes with offline decoding, RTL simulation is governed by a two-regime cost function---cross-fabric SoC paths are dominated by MMIO bus stalls (84.7% of call latency) whereas local subsystem paths are dominated by on-core vsprintf formatting (61.7%)---and requires live, zero-simulation-time string reconstruction to drive UVM scoreboards. We present a host-side in-simulation tokenized logging architecture for heterogeneous multi-core SoCs that eliminates both bottlenecks with zero firmware call-site edits. Using link-time ELF format-string virtual addresses as tokens over a fixed 32-bit word-aligned slot protocol across Arm, RISC-V, and DSP cores, on-core logging collapses to a single-pass specifier scan and $(1+S)$ MMIO word writes. This removes all on-core radix conversion ($C_{\mathrm{fmt}} \to 0$), 85.0%--94.5% of deployed bus transactions, 84.6%--93.3% of MMIO bus-stall cycles, and 63.5%--90.9% of total on-core logging cycles ($2.74\times$--$11.03\times$), while a SystemVerilog monitor reconstructs bit-exact strings in zero simulated cycles ($Δt_{\mathrm{sim}} = 0$). Across 858 matched full-chip test pairs (1,716 paired runs across 1,972 runs), we achieve 100% output parity with zero simulator memory overhead, saving 1,314.85 CPU hours (-26.18%) per regression pass, cutting median test CPU time by 41.51% (Suite A) and 21.33% (Suite B), and shrinking traces by 42.84% and waveforms by 28.82%.

cs.AR↗

A Refined SVD Algorithm for Collaborative Filtering

Collaborative filtering tries to predict the ratings of a user over some items based on opinions of other users with similar taste. The ratings are usually given in the form of a sparse matrix, the goal being to find the missing entries (i.e. ratings). Various approaches to collaborative filtering exist, some of the most popular ones being the Singular Value Decomposition (SVD) and K-means clustering. One of the challenges in the SVD approach is finding a good initialization of the unknown ratings. A possible initialization is suggested by [1]. In this paper we explain how K-means approach can be used to achieve the further refinement of this initialization for SVD. We show that our technique outperforms both initialization techniques used separately.

cs.IR↗

The effect of ionic diffusion on extracellular potentials in neural tissue

In computational neuroscience, it is common to use the simplifying assumption that diffusive currents are negligible compared to Ohmic currents. However, endured periods of intense neural signaling may cause local ion concentration changes in the millimolar range. Theoretical studies have identified scenarios where steep concentration gradients give rise to diffusive currents that are of comparable magnitude with Ohmic currents, and where the simplifying assumption that diffusion can be neglected does not hold. We here propose a novel formalism for computing (1) the ion concentration dynamics and (2) the electrical potential in the extracellular space surrounding multi-compartmental neuron models or networks of such (e.g., the Blue-Brain simulator). We use this formalism to explore the effects that diffusive currents can have on the extracellular (ECS) potential surrounding a small population of active cortical neurons. Our key findings are: (i) Sustained periods of neuronal output (simulations were run for 84 s) could change local ECS ion concentrations by several mM, as observed experimentally. (ii) For large, but realistic, concentration gradients, diffusive currents in the ECS were of the same magnitude as Ohmic currents. (iii) Neuronal current sources could induce local changes in the ECS potential by a few mV, whereas diffusive currents could could induce local changes in the ECS potential by a few tens of a mV. Diffusive currents could thus have a quite significant impact on ECS potentials. (v) Potential variations caused by diffusive currents were quite slow, but could influence the comparable to those induced by Ohmic currents up to frequencies as high as 7Hz.

physics.bio-ph↗