3 research outputs found
ADC/DAC-Free Analog Acceleration of Deep Neural Networks with Frequency Transformation
The edge processing of deep neural networks (DNNs) is becoming increasingly
important due to its ability to extract valuable information directly at the
data source to minimize latency and energy consumption. Frequency-domain model
compression, such as with the Walsh-Hadamard transform (WHT), has been
identified as an efficient alternative. However, the benefits of
frequency-domain processing are often offset by the increased
multiply-accumulate (MAC) operations required. This paper proposes a novel
approach to an energy-efficient acceleration of frequency-domain neural
networks by utilizing analog-domain frequency-based tensor transformations. Our
approach offers unique opportunities to enhance computational efficiency,
resulting in several high-level advantages, including array micro-architecture
with parallelism, ADC/DAC-free analog computations, and increased output
sparsity. Our approach achieves more compact cells by eliminating the need for
trainable parameters in the transformation matrix. Moreover, our novel array
micro-architecture enables adaptive stitching of cells column-wise and
row-wise, thereby facilitating perfect parallelism in computations.
Additionally, our scheme enables ADC/DAC-free computations by training against
highly quantized matrix-vector products, leveraging the parameter-free nature
of matrix multiplications. Another crucial aspect of our design is its ability
to handle signed-bit processing for frequency-based transformations. This leads
to increased output sparsity and reduced digitization workload. On a
1616 crossbars, for 8-bit input processing, the proposed approach
achieves the energy efficiency of 1602 tera operations per second per Watt
(TOPS/W) without early termination strategy and 5311 TOPS/W with early
termination strategy at VDD = 0.8 V
Benchmarking and modeling of analog and digital SRAM in-memory computing architectures
In-memory-computing is emerging as an efficient hardware paradigm for deep
neural network accelerators at the edge, enabling to break the memory wall and
exploit massive computational parallelism. Two design models have surged:
analog in-memory-computing (AIMC) and digital in-memory-computing (DIMC),
offering a different design space in terms of accuracy, efficiency and dataflow
flexibility. This paper targets the fair comparison and benchmarking of both
approaches to guide future designs, through a.) an overview of published
architectures; b.) an analytical cost model for energy and throughput; c.)
scheduling of workloads on a variety of modeled IMC architectures for
end-to-end network efficiency analysis, offering valuable workload-hardware
co-design insights