This paper presents SP-HD, a near-sensor image classification architecture that combines stochastic computing (SC) and hyperdimensional computing (HDC) to enable energy-efficient and compact embedded intelligence. The proposed approach introduces a stochastic projection mechanism that converts input features into bitstreams, enabling bipolar multiplications to be performed with simple logic and in-memory accumulation, thereby eliminating costly multipliers and level hypervectors. A mixed-signal ReRAM-based implementation further reduces data movement by performing projection and accumulation directly within the memory fabric, while binary-weight classification minimizes circuit complexity. SP-HD achieves competitive accuracy across multiple image datasets and delivers 3μJ energy per inference with a compact 2.56mm2hardware footprint, significantly outperforming prior ReRAM compute-in-memory accelerators in both energy and area efficiency.

Processing-in-Memory (PIM) enhances memory with computational capabilities, potentially solving energy and latency issues associated with data transfer between memory and processors. However, managing concurrent computation and data flow within the PIM architecture incurs significant latency and energy penalty for applications. This paper introduces Shared-PIM, an architecture for in-DRAM PIM that strategically allocates rows in memory banks, bolstered by memory peripherals, for concurrent processing and data movement. Shared-PIM enables simultaneous computation and data transfer within a memory bank. When compared to LISA, a state-of-the-art architecture that facilitates data transfers for in-DRAM PIM, Shared-PIM reduces data movement latency and energy by 5× and 1.2×, respectively. Furthermore, when integrated to a state-of-the-art (SOTA) in-DRAM PIM architecture (pLUTo), Shared-PIM achieves 1.4× faster addition and multiplication, and thereby improves the performance of matrix multiplication (MM) tasks by 40%, polynomial multiplication (PMM) by 44%, and numeric number transfer (NTT) tasks by 31%. Moreover, for graph processing tasks like Breadth-First Search (BFS) and Depth-First Search (DFS), Shared-PIM achieves a 29% improvement in speed, all with an area overhead of just 7.16% compared to the baseline pLUTo.

Prostate Cancer (PC) has been shown to become an epidemic among men in the world. Early detection of PC is essential for treatment. Biopsies are often done to determine the Gleason score of PC which helps to predict the aggressiveness of PC. As biopsies may cause harm especially for old people, machine learning can be used to predict the Gleason grade of PC from clinical biomarkers that are typically structured in a table. In this paper, we present a comparative study of various machine learning methods to detect the Gleason grade of PC from tabular data. We also investigate the performance of advanced deep learning architectures specialized to deal with tabular data, such as TabNet, for this purpose. Moreover, we propose to build an ensemble of the best performing classifiers to grade PC with a promising performance.