Microscopic view of neural accelerator silicon
Architectural Primitives

The Silicon
Logic of Inference.

Moving beyond general-purpose GPU constraints. TechLexWise engineers the physical intersection where mathematical weights meet specialized gate arrays for sub-millisecond deep learning performance.

Acceleration Primitives

Standard compute architectures spend up to 70% of energy on data movement rather than arithmetic. Our accelerators prioritize dataflow efficiency, utilizing specialized primitives designed exclusively for tensor contractions.

01 / ARCHITECTURE

Bit-Serial Processing

Unlike fixed-precison ALUs, bit-serial processing allows for dynamic precision scaling. By processing weights bit-by-bit, we enable variable quantatization that shifts according to the specific accuracy requirements of each neural layer.

Review Logic Syntheses
Bit-serial logic diagram
02 / INTERCONNECT

Systolic Array Optimization

Our systolic arrays eliminate the global memory bottleneck by passing data through a grid of locally coupled processing elements. This drastically reduces the energy cost per Multiply-Accumulate (MAC) operation.

  • Data Reusability: 98%
  • Bus Contention: 0%
  • Latency: Fixed
  • Scalability: Vertical

Sparse Matrix Logic

Deep learning models are inherently redundant. TechLexWise implements hardware-level sparsity support, skipping zero-valued weights entirely to compress workloads and surge throughput without the need for additional silicon area.

High-speed data interconnects
Selection Guide

Architectural Pathfinding

Specialization requires a commitment to either flexibility or fixed-function maximums. We help teams evaluate the Total Cost of Ownership (TCO) between field-programmable logic and application-specific silicon.

Our Quality Standard

All acceleration claims are supported by cycle-accurate latency or throughput benchmarks within our Toronto-based validation lab.

FPGA Implementation

FLEX-CORE
  • Time-to-Market: Rapid deployment via RTL reprogramming.
  • Adaptability: Reconfigure hardware as model architectures evolve.
  • Ideal for: Pilot clusters and research-intensive DL cycles.

TRADE-OFF: HIGHER POWER PER UNIT OPS

ASIC Development

MAX-DENSITY
  • Efficiency: Peak TOPS/W optimization for static logic.
  • Silicon Area: Minimal die footprint for high-density racks.
  • Ideal for: High-volume deployment at the edge or datacenter.

TRADE-OFF: SIGNIFICANT INITIAL NRE COSTS

Direct Architecture Comparison

Interact with our selection logic to filter results by your power constraints.

90% Latency Reduction
12-Bit Dynamic Precision
< 1ms Inference Delay
TOPS/W Max Efficiency
Hardware validation facility

MAPPING NEURAL OPS

Bridging the efficiency gap through co-design.

Methodology Notes

Quantization Audit

Post-Training Quantization (PTQ)

We specialize in weight and activation quantization to reduce memory traffic. This process maps high-precision floating point values to lower-bit integers (Int8/Int4) with minimal accuracy degradation.

Algorithmic Pruning

Removing non-critical neuronic connections within the model topology allows for sparse processing, directly translating to higher frames-per-second and lower power draw on custom hardware.

View Synthesis Process Request Audit

Building for the next billion parameters.

Custom Accelerator Architecture

For enterprises developing proprietary silicon from the ground up. We provide the RTL logic and architectural verified blueprints required for modern DL acceleration.

Learn more

Cycle-Accurate Benchmarking

Our simulations track every gate transition for maximum power accuracy. We rely on standard EDA tool protocols to ensure your hardware performs exactly as predicted.

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